Drug Therapy

From the Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA.

Clinical inertia is defined as the failure of clinicians to initiate or escalate guideline-directed medical therapy to achieve treatment goals for well-defined clinical conditions.^1,2 Evidence-based guidelines recommend optimal disease management with readily available medical therapies throughout the phases of clinical care. Unfortunately, the care provided to individual patients undergoes multiple modifications throughout the disease course, resulting in divergent pathways, significant deviations from treatment guidelines, and failure of “safeguard” checkpoints to reinstate, initiate, optimize, or stop treatments. Clinical inertia generally describes rigidity or resistance to change around implementing evidence-based guidelines. Furthermore, this term describes treatment behavior on the part of an individual clinician, not organizational inertia, which generally encompasses both internal (immediate clinical practice settings) and external factors (national and international guidelines and recommendations), eventually leading to resistance to optimizing disease treatment and therapeutic regimens. Individual clinicians’ clinical inertia in the form of resistance to guideline implementation and evidence-based principles can be one factor that drives organizational inertia. In turn, such individual behavior can be dictated by personal beliefs, knowledge, interpretation, skills, management principles, and biases. The terms therapeutic inertia or clinical inertia should not be confused with nonadherence from the patient’s standpoint when the clinician follows the best practice guidelines.³

Clinical inertia has been described in several clinical domains, including diabetes,^4,5 hypertension,^6,7 heart failure,⁸ depression,⁹ pulmonary medicine,¹⁰ and complex disease management.¹¹ Clinicians can set suboptimal treatment goals due to specific beliefs and attitudes around optimal therapeutic goals. For example, when treating a patient with a chronic disease that is presently stable, a clinician could elect to initiate suboptimal treatment, as escalation of treatment might not be the priority in stable disease; they also may have concerns about overtreatment. Other factors that can contribute to clinical inertia (ie, undertreatment in the presence of indications for treatment) include those related to the patient, the clinical setting, and the organization, along with the importance of individualizing therapies in specific patients. Organizational inertia is the initial global resistance by the system to implementation, which can slow the dissemination and adaptation of best practices but eventually declines over time. Individual clinical inertia, on the other hand, will likely persist after the system-level rollout of guideline-based approaches.

The trajectory of dissemination, implementation, and adaptation of innovations and best practices is illustrated in the Figure. When the guidelines and medical societies endorse the adaptation of innovations or practice change after the benefits of such innovations/change have been established by the regulatory bodies, uptake can be hindered by both organizational and clinical inertia. Overcoming inertia to system-level changes requires addressing individual clinicians, along with practice and organizational factors, in order to ensure systematic adaptations. From the clinicians’ view, training and cognitive interventions to improve the adaptation and coping skills can improve understanding of treatment options through standardized educational and behavioral modification tools, direct and indirect feedback around performance, and decision support through a continuous improvement approach on both individual and system levels.

Addressing inertia in clinical practice requires a deep understanding of the individual and organizational elements that foster resistance to adapting best practice models. Research that explores tools and approaches to overcome inertia in managing complex diseases is a key step in advancing clinical innovation and disseminating best practices.

Corresponding author: Ebrahim Barkoudah, MD, MPH; ebarkoudah@bwh.harvard.edu

Disclosures: None reported.

From the Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA.

Clinical inertia is defined as the failure of clinicians to initiate or escalate guideline-directed medical therapy to achieve treatment goals for well-defined clinical conditions.^1,2 Evidence-based guidelines recommend optimal disease management with readily available medical therapies throughout the phases of clinical care. Unfortunately, the care provided to individual patients undergoes multiple modifications throughout the disease course, resulting in divergent pathways, significant deviations from treatment guidelines, and failure of “safeguard” checkpoints to reinstate, initiate, optimize, or stop treatments. Clinical inertia generally describes rigidity or resistance to change around implementing evidence-based guidelines. Furthermore, this term describes treatment behavior on the part of an individual clinician, not organizational inertia, which generally encompasses both internal (immediate clinical practice settings) and external factors (national and international guidelines and recommendations), eventually leading to resistance to optimizing disease treatment and therapeutic regimens. Individual clinicians’ clinical inertia in the form of resistance to guideline implementation and evidence-based principles can be one factor that drives organizational inertia. In turn, such individual behavior can be dictated by personal beliefs, knowledge, interpretation, skills, management principles, and biases. The terms therapeutic inertia or clinical inertia should not be confused with nonadherence from the patient’s standpoint when the clinician follows the best practice guidelines.³

Clinical inertia has been described in several clinical domains, including diabetes,^4,5 hypertension,^6,7 heart failure,⁸ depression,⁹ pulmonary medicine,¹⁰ and complex disease management.¹¹ Clinicians can set suboptimal treatment goals due to specific beliefs and attitudes around optimal therapeutic goals. For example, when treating a patient with a chronic disease that is presently stable, a clinician could elect to initiate suboptimal treatment, as escalation of treatment might not be the priority in stable disease; they also may have concerns about overtreatment. Other factors that can contribute to clinical inertia (ie, undertreatment in the presence of indications for treatment) include those related to the patient, the clinical setting, and the organization, along with the importance of individualizing therapies in specific patients. Organizational inertia is the initial global resistance by the system to implementation, which can slow the dissemination and adaptation of best practices but eventually declines over time. Individual clinical inertia, on the other hand, will likely persist after the system-level rollout of guideline-based approaches.

The trajectory of dissemination, implementation, and adaptation of innovations and best practices is illustrated in the Figure. When the guidelines and medical societies endorse the adaptation of innovations or practice change after the benefits of such innovations/change have been established by the regulatory bodies, uptake can be hindered by both organizational and clinical inertia. Overcoming inertia to system-level changes requires addressing individual clinicians, along with practice and organizational factors, in order to ensure systematic adaptations. From the clinicians’ view, training and cognitive interventions to improve the adaptation and coping skills can improve understanding of treatment options through standardized educational and behavioral modification tools, direct and indirect feedback around performance, and decision support through a continuous improvement approach on both individual and system levels.

Addressing inertia in clinical practice requires a deep understanding of the individual and organizational elements that foster resistance to adapting best practice models. Research that explores tools and approaches to overcome inertia in managing complex diseases is a key step in advancing clinical innovation and disseminating best practices.

Corresponding author: Ebrahim Barkoudah, MD, MPH; ebarkoudah@bwh.harvard.edu

Disclosures: None reported.

From the Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA.

Clinical inertia is defined as the failure of clinicians to initiate or escalate guideline-directed medical therapy to achieve treatment goals for well-defined clinical conditions.^1,2 Evidence-based guidelines recommend optimal disease management with readily available medical therapies throughout the phases of clinical care. Unfortunately, the care provided to individual patients undergoes multiple modifications throughout the disease course, resulting in divergent pathways, significant deviations from treatment guidelines, and failure of “safeguard” checkpoints to reinstate, initiate, optimize, or stop treatments. Clinical inertia generally describes rigidity or resistance to change around implementing evidence-based guidelines. Furthermore, this term describes treatment behavior on the part of an individual clinician, not organizational inertia, which generally encompasses both internal (immediate clinical practice settings) and external factors (national and international guidelines and recommendations), eventually leading to resistance to optimizing disease treatment and therapeutic regimens. Individual clinicians’ clinical inertia in the form of resistance to guideline implementation and evidence-based principles can be one factor that drives organizational inertia. In turn, such individual behavior can be dictated by personal beliefs, knowledge, interpretation, skills, management principles, and biases. The terms therapeutic inertia or clinical inertia should not be confused with nonadherence from the patient’s standpoint when the clinician follows the best practice guidelines.³

Clinical inertia has been described in several clinical domains, including diabetes,^4,5 hypertension,^6,7 heart failure,⁸ depression,⁹ pulmonary medicine,¹⁰ and complex disease management.¹¹ Clinicians can set suboptimal treatment goals due to specific beliefs and attitudes around optimal therapeutic goals. For example, when treating a patient with a chronic disease that is presently stable, a clinician could elect to initiate suboptimal treatment, as escalation of treatment might not be the priority in stable disease; they also may have concerns about overtreatment. Other factors that can contribute to clinical inertia (ie, undertreatment in the presence of indications for treatment) include those related to the patient, the clinical setting, and the organization, along with the importance of individualizing therapies in specific patients. Organizational inertia is the initial global resistance by the system to implementation, which can slow the dissemination and adaptation of best practices but eventually declines over time. Individual clinical inertia, on the other hand, will likely persist after the system-level rollout of guideline-based approaches.

The trajectory of dissemination, implementation, and adaptation of innovations and best practices is illustrated in the Figure. When the guidelines and medical societies endorse the adaptation of innovations or practice change after the benefits of such innovations/change have been established by the regulatory bodies, uptake can be hindered by both organizational and clinical inertia. Overcoming inertia to system-level changes requires addressing individual clinicians, along with practice and organizational factors, in order to ensure systematic adaptations. From the clinicians’ view, training and cognitive interventions to improve the adaptation and coping skills can improve understanding of treatment options through standardized educational and behavioral modification tools, direct and indirect feedback around performance, and decision support through a continuous improvement approach on both individual and system levels.

Addressing inertia in clinical practice requires a deep understanding of the individual and organizational elements that foster resistance to adapting best practice models. Research that explores tools and approaches to overcome inertia in managing complex diseases is a key step in advancing clinical innovation and disseminating best practices.

Corresponding author: Ebrahim Barkoudah, MD, MPH; ebarkoudah@bwh.harvard.edu

Disclosures: None reported.

Study 1 Overview (STICHES Investigators)

Objective: To assess the survival benefit of coronary-artery bypass grafting (CABG) added to guideline-directed medical therapy, compared to optimal medical therapy (OMT) alone, in patients with coronary artery disease, heart failure, and severe left ventricular dysfunction. Design: Multicenter, randomized, prospective study with extended follow-up (median duration of 9.8 years).

Setting and participants: A total of 1212 patients with left ventricular ejection fraction (LVEF) of 35% or less and coronary artery disease were randomized to medical therapy plus CABG or OMT alone at 127 clinical sites in 26 countries.

Main outcome measures: The primary endpoint was death from any cause. Main secondary endpoints were death from cardiovascular causes and a composite outcome of death from any cause or hospitalization for cardiovascular causes.

Main results: There were 359 primary outcome all-cause deaths (58.9%) in the CABG group and 398 (66.1%) in the medical therapy group (hazard ratio [HR], 0.84; 95% CI, 0.73-0.97; P = .02). Death from cardiovascular causes was reported in 247 patients (40.5%) in the CABG group and 297 patients (49.3%) in the medical therapy group (HR, 0.79; 95% CI, 0.66-0.93; P < .01). The composite outcome of death from any cause or hospitalization for cardiovascular causes occurred in 467 patients (76.6%) in the CABG group and 467 patients (87.0%) in the medical therapy group (HR, 0.72; 95% CI, 0.64-0.82; P < .01).

Conclusion: Over a median follow-up of 9.8 years in patients with ischemic cardiomyopathy with severely reduced ejection fraction, the rates of death from any cause, death from cardiovascular causes, and the composite of death from any cause or hospitalization for cardiovascular causes were significantly lower in patients undergoing CABG than in patients receiving medical therapy alone.

Study 2 Overview (REVIVED BCIS Trial Group)

Objective: To assess whether percutaneous coronary intervention (PCI) can improve survival and left ventricular function in patients with severe left ventricular systolic dysfunction as compared to OMT alone.

Design: Multicenter, randomized, prospective study.

Setting and participants: A total of 700 patients with LVEF <35% with severe coronary artery disease amendable to PCI and demonstrable myocardial viability were randomly assigned to either PCI plus optimal medical therapy (PCI group) or OMT alone (OMT group).

Main outcome measures: The primary outcome was death from any cause or hospitalization for heart failure. The main secondary outcomes were LVEF at 6 and 12 months and quality of life (QOL) scores.

Main results: Over a median follow-up of 41 months, the primary outcome was reported in 129 patients (37.2%) in the PCI group and in 134 patients (38.0%) in the OMT group (HR, 0.99; 95% CI, 0.78-1.27; P = .96). The LVEF was similar in the 2 groups at 6 months (mean difference, –1.6 percentage points; 95% CI, –3.7 to 0.5) and at 12 months (mean difference, 0.9 percentage points; 95% CI, –1.7 to 3.4). QOL scores at 6 and 12 months favored the PCI group, but the difference had diminished at 24 months.

Conclusion: In patients with severe ischemic cardiomyopathy, revascularization by PCI in addition to OMT did not result in a lower incidence of death from any cause or hospitalization from heart failure.

Commentary

Coronary artery disease is the most common cause of heart failure with reduced ejection fraction and an important cause of mortality.¹ Patients with ischemic cardiomyopathy with reduced ejection fraction are often considered for revascularization in addition to OMT and device therapies. Although there have been multiple retrospective studies and registries suggesting that cardiac outcomes and LVEF improve with revascularization, the number of large-scale prospective studies that assessed this clinical question and randomized patients to revascularization plus OMT compared to OMT alone has been limited.

In the Surgical Treatment for Ischemic Heart Failure (STICH) study,^2,3 eligible patients had coronary artery disease amendable to CABG and a LVEF of 35% or less. Patients (N = 1212) were randomly assigned to CABG plus OMT or OMT alone between July 2002 and May 2007. The original study, with a median follow-up of 5 years, did not show survival benefit, but the investigators reported that the primary outcome of death from any cause was significantly lower in the CABG group compared to OMT alone when follow-up of the same study population was extended to 9.8 years (58.9% vs 66.1%, P = .02). The findings from this study led to a class I guideline recommendation of CABG over medical therapy in patients with multivessel disease and low ejection fraction.⁴

Since the STICH trial was designed, there have been significant improvements in devices and techniques used for PCI, and the procedure is now widely performed in patients with multivessel disease.⁵ The advantages of PCI over CABG include shorter recovery times and lower risk of immediate complications. In this context, the recently reported Revascularization for Ischemic Ventricular Dysfunction (REVIVED) study assessed clinical outcomes in patients with severe coronary artery disease and reduced ejection fraction by randomizing patients to either PCI with OMT or OMT alone.⁶ At a median follow-up of 3.5 years, the investigators found no difference in the primary outcome of death from any cause or hospitalization for heart failure (37.2% vs 38.0%; 95% CI, 0.78-1.28; P = .96). Moreover, the degree of LVEF improvement, assessed by follow-up echocardiogram read by the core lab, showed no difference in the degree of LVEF improvement between groups at 6 and 12 months. Finally, although results of the QOL assessment using the Kansas City Cardiomyopathy Questionnaire (KCCQ), a validated, patient-reported, heart-failure-specific QOL scale, favored the PCI group at 6 and 12 months of follow-up, the difference had diminished at 24 months.

The main strength of the REVIVED study was that it targeted a patient population with severe coronary artery disease, including left main disease and severely reduced ejection fraction, that historically have been excluded from large-scale randomized controlled studies evaluating PCI with OMT compared to OMT alone.⁷ However, there are several points to consider when interpreting the results of this study. First, further details of the PCI procedures are necessary. The REVIVED study recommended revascularization of all territories with viable myocardium; the anatomical revascularization index utilizing the British Cardiovascular Intervention Society (BCIS) Jeopardy Score was 71%. It is important to note that this jeopardy score was operator-reported and the core-lab adjudicated anatomical revascularization rate may be lower. Although viability testing primarily utilizing cardiac magnetic resonance imaging was performed in most patients, correlation between the revascularization territory and the viable segments has yet to be reported. Moreover, procedural details such as use of intravascular ultrasound and physiological testing, known to improve clinical outcome, need to be reported.^8,9

Second, there is a high prevalence of ischemic cardiomyopathy, and it is important to note that the patients included in this study were highly selected from daily clinical practice, as evidenced by the prolonged enrollment period (8 years). Individuals were largely stable patients with less complex coronary anatomy as evidenced by the median interval from angiography to randomization of 80 days. Taking into consideration the degree of left ventricular dysfunction for patients included in the trial, only 14% of the patients had left main disease and half of the patients only had 2-vessel disease. The severity of the left main disease also needs to be clarified as it is likely that patients the operator determined to be critical were not enrolled in the study. Furthermore, the standard of care based on the STICH trial is to refer patients with severe multivessel coronary artery disease to CABG, making it more likely that patients with more severe and complex disease were not included in this trial. It is also important to note that this study enrolled patients with stable ischemic heart disease, and the data do not apply to patients presenting with acute coronary syndrome.

Third, although the primary outcome was similar between the groups, the secondary outcome of unplanned revascularization was lower in the PCI group. In addition, the rate of acute myocardial infarction (MI) was similar between the 2 groups, but the rate of spontaneous MI was lower in the PCI group compared to the OMT group (5.2% vs 9.3%) as 40% of MI cases in the PCI group were periprocedural MIs. The correlation between periprocedural MI and long-term outcomes has been modest compared to spontaneous MI. Moreover, with the longer follow-up, the number of spontaneous MI cases is expected to rise while the number of periprocedural MI cases is not. Extending the follow-up period is also important, as the STICH extension trial showed a statistically significant difference at 10-year follow up despite negative results at the time of the original publication.

Fourth, the REVIVED trial randomized a significantly lower number of patients compared to the STICH trial, and the authors reported fewer primary-outcome events than the estimated number needed to achieve the power to assess the primary hypothesis. In addition, significant improvements in medical treatment for heart failure with reduced ejection fraction since the STICH trial make comparison of PCI vs CABG in this patient population unfeasible.

Finally, although severe angina was not an exclusion criterion, two-thirds of the patients enrolled had no angina, and only 2% of the patients had baseline severe angina. This is important to consider when interpreting the results of the patient-reported health status as previous studies have shown that patients with worse angina at baseline derive the largest improvement in their QOL,^10,11 and symptom improvement is the main indication for PCI in patients with stable ischemic heart disease.

Applications for Clinical Practice and System Implementation

In patients with severe left ventricular systolic dysfunction and multivessel stable ischemic heart disease who are well compensated and have little or no angina at baseline, OMT alone as an initial strategy may be considered against the addition of PCI after careful risk and benefit discussion. Further details about revascularization and extended follow-up data from the REVIVED trial are necessary.

Practice Points

• Patients with ischemic cardiomyopathy with reduced ejection fraction have been an understudied population in previous studies.
• Further studies are necessary to understand the benefits of revascularization and the role of viability testing in this population.

– Taishi Hirai MD, and Ziad Sayed Ahmad, MD
University of Missouri, Columbia, MO

Study 1 Overview (STICHES Investigators)

Objective: To assess the survival benefit of coronary-artery bypass grafting (CABG) added to guideline-directed medical therapy, compared to optimal medical therapy (OMT) alone, in patients with coronary artery disease, heart failure, and severe left ventricular dysfunction. Design: Multicenter, randomized, prospective study with extended follow-up (median duration of 9.8 years).

Setting and participants: A total of 1212 patients with left ventricular ejection fraction (LVEF) of 35% or less and coronary artery disease were randomized to medical therapy plus CABG or OMT alone at 127 clinical sites in 26 countries.

Main outcome measures: The primary endpoint was death from any cause. Main secondary endpoints were death from cardiovascular causes and a composite outcome of death from any cause or hospitalization for cardiovascular causes.

Main results: There were 359 primary outcome all-cause deaths (58.9%) in the CABG group and 398 (66.1%) in the medical therapy group (hazard ratio [HR], 0.84; 95% CI, 0.73-0.97; P = .02). Death from cardiovascular causes was reported in 247 patients (40.5%) in the CABG group and 297 patients (49.3%) in the medical therapy group (HR, 0.79; 95% CI, 0.66-0.93; P < .01). The composite outcome of death from any cause or hospitalization for cardiovascular causes occurred in 467 patients (76.6%) in the CABG group and 467 patients (87.0%) in the medical therapy group (HR, 0.72; 95% CI, 0.64-0.82; P < .01).

Conclusion: Over a median follow-up of 9.8 years in patients with ischemic cardiomyopathy with severely reduced ejection fraction, the rates of death from any cause, death from cardiovascular causes, and the composite of death from any cause or hospitalization for cardiovascular causes were significantly lower in patients undergoing CABG than in patients receiving medical therapy alone.

Study 2 Overview (REVIVED BCIS Trial Group)

Objective: To assess whether percutaneous coronary intervention (PCI) can improve survival and left ventricular function in patients with severe left ventricular systolic dysfunction as compared to OMT alone.

Design: Multicenter, randomized, prospective study.

Setting and participants: A total of 700 patients with LVEF <35% with severe coronary artery disease amendable to PCI and demonstrable myocardial viability were randomly assigned to either PCI plus optimal medical therapy (PCI group) or OMT alone (OMT group).

Main outcome measures: The primary outcome was death from any cause or hospitalization for heart failure. The main secondary outcomes were LVEF at 6 and 12 months and quality of life (QOL) scores.

Main results: Over a median follow-up of 41 months, the primary outcome was reported in 129 patients (37.2%) in the PCI group and in 134 patients (38.0%) in the OMT group (HR, 0.99; 95% CI, 0.78-1.27; P = .96). The LVEF was similar in the 2 groups at 6 months (mean difference, –1.6 percentage points; 95% CI, –3.7 to 0.5) and at 12 months (mean difference, 0.9 percentage points; 95% CI, –1.7 to 3.4). QOL scores at 6 and 12 months favored the PCI group, but the difference had diminished at 24 months.

Conclusion: In patients with severe ischemic cardiomyopathy, revascularization by PCI in addition to OMT did not result in a lower incidence of death from any cause or hospitalization from heart failure.

Commentary

Coronary artery disease is the most common cause of heart failure with reduced ejection fraction and an important cause of mortality.¹ Patients with ischemic cardiomyopathy with reduced ejection fraction are often considered for revascularization in addition to OMT and device therapies. Although there have been multiple retrospective studies and registries suggesting that cardiac outcomes and LVEF improve with revascularization, the number of large-scale prospective studies that assessed this clinical question and randomized patients to revascularization plus OMT compared to OMT alone has been limited.

In the Surgical Treatment for Ischemic Heart Failure (STICH) study,^2,3 eligible patients had coronary artery disease amendable to CABG and a LVEF of 35% or less. Patients (N = 1212) were randomly assigned to CABG plus OMT or OMT alone between July 2002 and May 2007. The original study, with a median follow-up of 5 years, did not show survival benefit, but the investigators reported that the primary outcome of death from any cause was significantly lower in the CABG group compared to OMT alone when follow-up of the same study population was extended to 9.8 years (58.9% vs 66.1%, P = .02). The findings from this study led to a class I guideline recommendation of CABG over medical therapy in patients with multivessel disease and low ejection fraction.⁴

Since the STICH trial was designed, there have been significant improvements in devices and techniques used for PCI, and the procedure is now widely performed in patients with multivessel disease.⁵ The advantages of PCI over CABG include shorter recovery times and lower risk of immediate complications. In this context, the recently reported Revascularization for Ischemic Ventricular Dysfunction (REVIVED) study assessed clinical outcomes in patients with severe coronary artery disease and reduced ejection fraction by randomizing patients to either PCI with OMT or OMT alone.⁶ At a median follow-up of 3.5 years, the investigators found no difference in the primary outcome of death from any cause or hospitalization for heart failure (37.2% vs 38.0%; 95% CI, 0.78-1.28; P = .96). Moreover, the degree of LVEF improvement, assessed by follow-up echocardiogram read by the core lab, showed no difference in the degree of LVEF improvement between groups at 6 and 12 months. Finally, although results of the QOL assessment using the Kansas City Cardiomyopathy Questionnaire (KCCQ), a validated, patient-reported, heart-failure-specific QOL scale, favored the PCI group at 6 and 12 months of follow-up, the difference had diminished at 24 months.

The main strength of the REVIVED study was that it targeted a patient population with severe coronary artery disease, including left main disease and severely reduced ejection fraction, that historically have been excluded from large-scale randomized controlled studies evaluating PCI with OMT compared to OMT alone.⁷ However, there are several points to consider when interpreting the results of this study. First, further details of the PCI procedures are necessary. The REVIVED study recommended revascularization of all territories with viable myocardium; the anatomical revascularization index utilizing the British Cardiovascular Intervention Society (BCIS) Jeopardy Score was 71%. It is important to note that this jeopardy score was operator-reported and the core-lab adjudicated anatomical revascularization rate may be lower. Although viability testing primarily utilizing cardiac magnetic resonance imaging was performed in most patients, correlation between the revascularization territory and the viable segments has yet to be reported. Moreover, procedural details such as use of intravascular ultrasound and physiological testing, known to improve clinical outcome, need to be reported.^8,9

Second, there is a high prevalence of ischemic cardiomyopathy, and it is important to note that the patients included in this study were highly selected from daily clinical practice, as evidenced by the prolonged enrollment period (8 years). Individuals were largely stable patients with less complex coronary anatomy as evidenced by the median interval from angiography to randomization of 80 days. Taking into consideration the degree of left ventricular dysfunction for patients included in the trial, only 14% of the patients had left main disease and half of the patients only had 2-vessel disease. The severity of the left main disease also needs to be clarified as it is likely that patients the operator determined to be critical were not enrolled in the study. Furthermore, the standard of care based on the STICH trial is to refer patients with severe multivessel coronary artery disease to CABG, making it more likely that patients with more severe and complex disease were not included in this trial. It is also important to note that this study enrolled patients with stable ischemic heart disease, and the data do not apply to patients presenting with acute coronary syndrome.

Third, although the primary outcome was similar between the groups, the secondary outcome of unplanned revascularization was lower in the PCI group. In addition, the rate of acute myocardial infarction (MI) was similar between the 2 groups, but the rate of spontaneous MI was lower in the PCI group compared to the OMT group (5.2% vs 9.3%) as 40% of MI cases in the PCI group were periprocedural MIs. The correlation between periprocedural MI and long-term outcomes has been modest compared to spontaneous MI. Moreover, with the longer follow-up, the number of spontaneous MI cases is expected to rise while the number of periprocedural MI cases is not. Extending the follow-up period is also important, as the STICH extension trial showed a statistically significant difference at 10-year follow up despite negative results at the time of the original publication.

Fourth, the REVIVED trial randomized a significantly lower number of patients compared to the STICH trial, and the authors reported fewer primary-outcome events than the estimated number needed to achieve the power to assess the primary hypothesis. In addition, significant improvements in medical treatment for heart failure with reduced ejection fraction since the STICH trial make comparison of PCI vs CABG in this patient population unfeasible.

Finally, although severe angina was not an exclusion criterion, two-thirds of the patients enrolled had no angina, and only 2% of the patients had baseline severe angina. This is important to consider when interpreting the results of the patient-reported health status as previous studies have shown that patients with worse angina at baseline derive the largest improvement in their QOL,^10,11 and symptom improvement is the main indication for PCI in patients with stable ischemic heart disease.

Applications for Clinical Practice and System Implementation

In patients with severe left ventricular systolic dysfunction and multivessel stable ischemic heart disease who are well compensated and have little or no angina at baseline, OMT alone as an initial strategy may be considered against the addition of PCI after careful risk and benefit discussion. Further details about revascularization and extended follow-up data from the REVIVED trial are necessary.

Practice Points

• Patients with ischemic cardiomyopathy with reduced ejection fraction have been an understudied population in previous studies.
• Further studies are necessary to understand the benefits of revascularization and the role of viability testing in this population.

– Taishi Hirai MD, and Ziad Sayed Ahmad, MD
University of Missouri, Columbia, MO

Study 1 Overview (STICHES Investigators)

Objective: To assess the survival benefit of coronary-artery bypass grafting (CABG) added to guideline-directed medical therapy, compared to optimal medical therapy (OMT) alone, in patients with coronary artery disease, heart failure, and severe left ventricular dysfunction. Design: Multicenter, randomized, prospective study with extended follow-up (median duration of 9.8 years).

Setting and participants: A total of 1212 patients with left ventricular ejection fraction (LVEF) of 35% or less and coronary artery disease were randomized to medical therapy plus CABG or OMT alone at 127 clinical sites in 26 countries.

Main outcome measures: The primary endpoint was death from any cause. Main secondary endpoints were death from cardiovascular causes and a composite outcome of death from any cause or hospitalization for cardiovascular causes.

Main results: There were 359 primary outcome all-cause deaths (58.9%) in the CABG group and 398 (66.1%) in the medical therapy group (hazard ratio [HR], 0.84; 95% CI, 0.73-0.97; P = .02). Death from cardiovascular causes was reported in 247 patients (40.5%) in the CABG group and 297 patients (49.3%) in the medical therapy group (HR, 0.79; 95% CI, 0.66-0.93; P < .01). The composite outcome of death from any cause or hospitalization for cardiovascular causes occurred in 467 patients (76.6%) in the CABG group and 467 patients (87.0%) in the medical therapy group (HR, 0.72; 95% CI, 0.64-0.82; P < .01).

Conclusion: Over a median follow-up of 9.8 years in patients with ischemic cardiomyopathy with severely reduced ejection fraction, the rates of death from any cause, death from cardiovascular causes, and the composite of death from any cause or hospitalization for cardiovascular causes were significantly lower in patients undergoing CABG than in patients receiving medical therapy alone.

Study 2 Overview (REVIVED BCIS Trial Group)

Objective: To assess whether percutaneous coronary intervention (PCI) can improve survival and left ventricular function in patients with severe left ventricular systolic dysfunction as compared to OMT alone.

Design: Multicenter, randomized, prospective study.

Setting and participants: A total of 700 patients with LVEF <35% with severe coronary artery disease amendable to PCI and demonstrable myocardial viability were randomly assigned to either PCI plus optimal medical therapy (PCI group) or OMT alone (OMT group).

Main outcome measures: The primary outcome was death from any cause or hospitalization for heart failure. The main secondary outcomes were LVEF at 6 and 12 months and quality of life (QOL) scores.

Main results: Over a median follow-up of 41 months, the primary outcome was reported in 129 patients (37.2%) in the PCI group and in 134 patients (38.0%) in the OMT group (HR, 0.99; 95% CI, 0.78-1.27; P = .96). The LVEF was similar in the 2 groups at 6 months (mean difference, –1.6 percentage points; 95% CI, –3.7 to 0.5) and at 12 months (mean difference, 0.9 percentage points; 95% CI, –1.7 to 3.4). QOL scores at 6 and 12 months favored the PCI group, but the difference had diminished at 24 months.

Conclusion: In patients with severe ischemic cardiomyopathy, revascularization by PCI in addition to OMT did not result in a lower incidence of death from any cause or hospitalization from heart failure.

Commentary

Coronary artery disease is the most common cause of heart failure with reduced ejection fraction and an important cause of mortality.¹ Patients with ischemic cardiomyopathy with reduced ejection fraction are often considered for revascularization in addition to OMT and device therapies. Although there have been multiple retrospective studies and registries suggesting that cardiac outcomes and LVEF improve with revascularization, the number of large-scale prospective studies that assessed this clinical question and randomized patients to revascularization plus OMT compared to OMT alone has been limited.

In the Surgical Treatment for Ischemic Heart Failure (STICH) study,^2,3 eligible patients had coronary artery disease amendable to CABG and a LVEF of 35% or less. Patients (N = 1212) were randomly assigned to CABG plus OMT or OMT alone between July 2002 and May 2007. The original study, with a median follow-up of 5 years, did not show survival benefit, but the investigators reported that the primary outcome of death from any cause was significantly lower in the CABG group compared to OMT alone when follow-up of the same study population was extended to 9.8 years (58.9% vs 66.1%, P = .02). The findings from this study led to a class I guideline recommendation of CABG over medical therapy in patients with multivessel disease and low ejection fraction.⁴

Since the STICH trial was designed, there have been significant improvements in devices and techniques used for PCI, and the procedure is now widely performed in patients with multivessel disease.⁵ The advantages of PCI over CABG include shorter recovery times and lower risk of immediate complications. In this context, the recently reported Revascularization for Ischemic Ventricular Dysfunction (REVIVED) study assessed clinical outcomes in patients with severe coronary artery disease and reduced ejection fraction by randomizing patients to either PCI with OMT or OMT alone.⁶ At a median follow-up of 3.5 years, the investigators found no difference in the primary outcome of death from any cause or hospitalization for heart failure (37.2% vs 38.0%; 95% CI, 0.78-1.28; P = .96). Moreover, the degree of LVEF improvement, assessed by follow-up echocardiogram read by the core lab, showed no difference in the degree of LVEF improvement between groups at 6 and 12 months. Finally, although results of the QOL assessment using the Kansas City Cardiomyopathy Questionnaire (KCCQ), a validated, patient-reported, heart-failure-specific QOL scale, favored the PCI group at 6 and 12 months of follow-up, the difference had diminished at 24 months.

The main strength of the REVIVED study was that it targeted a patient population with severe coronary artery disease, including left main disease and severely reduced ejection fraction, that historically have been excluded from large-scale randomized controlled studies evaluating PCI with OMT compared to OMT alone.⁷ However, there are several points to consider when interpreting the results of this study. First, further details of the PCI procedures are necessary. The REVIVED study recommended revascularization of all territories with viable myocardium; the anatomical revascularization index utilizing the British Cardiovascular Intervention Society (BCIS) Jeopardy Score was 71%. It is important to note that this jeopardy score was operator-reported and the core-lab adjudicated anatomical revascularization rate may be lower. Although viability testing primarily utilizing cardiac magnetic resonance imaging was performed in most patients, correlation between the revascularization territory and the viable segments has yet to be reported. Moreover, procedural details such as use of intravascular ultrasound and physiological testing, known to improve clinical outcome, need to be reported.^8,9

Second, there is a high prevalence of ischemic cardiomyopathy, and it is important to note that the patients included in this study were highly selected from daily clinical practice, as evidenced by the prolonged enrollment period (8 years). Individuals were largely stable patients with less complex coronary anatomy as evidenced by the median interval from angiography to randomization of 80 days. Taking into consideration the degree of left ventricular dysfunction for patients included in the trial, only 14% of the patients had left main disease and half of the patients only had 2-vessel disease. The severity of the left main disease also needs to be clarified as it is likely that patients the operator determined to be critical were not enrolled in the study. Furthermore, the standard of care based on the STICH trial is to refer patients with severe multivessel coronary artery disease to CABG, making it more likely that patients with more severe and complex disease were not included in this trial. It is also important to note that this study enrolled patients with stable ischemic heart disease, and the data do not apply to patients presenting with acute coronary syndrome.

Third, although the primary outcome was similar between the groups, the secondary outcome of unplanned revascularization was lower in the PCI group. In addition, the rate of acute myocardial infarction (MI) was similar between the 2 groups, but the rate of spontaneous MI was lower in the PCI group compared to the OMT group (5.2% vs 9.3%) as 40% of MI cases in the PCI group were periprocedural MIs. The correlation between periprocedural MI and long-term outcomes has been modest compared to spontaneous MI. Moreover, with the longer follow-up, the number of spontaneous MI cases is expected to rise while the number of periprocedural MI cases is not. Extending the follow-up period is also important, as the STICH extension trial showed a statistically significant difference at 10-year follow up despite negative results at the time of the original publication.

Fourth, the REVIVED trial randomized a significantly lower number of patients compared to the STICH trial, and the authors reported fewer primary-outcome events than the estimated number needed to achieve the power to assess the primary hypothesis. In addition, significant improvements in medical treatment for heart failure with reduced ejection fraction since the STICH trial make comparison of PCI vs CABG in this patient population unfeasible.

Finally, although severe angina was not an exclusion criterion, two-thirds of the patients enrolled had no angina, and only 2% of the patients had baseline severe angina. This is important to consider when interpreting the results of the patient-reported health status as previous studies have shown that patients with worse angina at baseline derive the largest improvement in their QOL,^10,11 and symptom improvement is the main indication for PCI in patients with stable ischemic heart disease.

Applications for Clinical Practice and System Implementation

In patients with severe left ventricular systolic dysfunction and multivessel stable ischemic heart disease who are well compensated and have little or no angina at baseline, OMT alone as an initial strategy may be considered against the addition of PCI after careful risk and benefit discussion. Further details about revascularization and extended follow-up data from the REVIVED trial are necessary.

Practice Points

• Patients with ischemic cardiomyopathy with reduced ejection fraction have been an understudied population in previous studies.
• Further studies are necessary to understand the benefits of revascularization and the role of viability testing in this population.

– Taishi Hirai MD, and Ziad Sayed Ahmad, MD
University of Missouri, Columbia, MO

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: amungmode@t1dexchange.org

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: amungmode@t1dexchange.org

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: amungmode@t1dexchange.org

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

Dr. George Sakoulas is an infectious diseases clinician at Sharp Memorial Hospital in San Diego and professor of pediatrics at the University of California, San Diego School of Medicine. He was the lead investigator in a study published in the May/June 2022 issue of JCOM that found that, when allocated to the appropriate patient type, intravenous immunoglobulin can reduce hospital costs for COVID-19 care. ¹ He joined JCOM’s Editor-in-Chief, Dr. Ebrahim Barkoudah, to discuss the study’s background and highlight its main findings.

The following has been edited for length and clarity.

Dr. Barkoudah Dr. Sakoulas is an investigator and a clinician, bridging both worlds to bring the best evidence to our patients. We’re discussing his new article regarding intravenous immunoglobulin in treating nonventilated COVID-19 patients with moderate-to-severe hypoxia. Dr. Sakoulas, could you please share with our readers the clinical question your study addressed and what your work around COVID-19 management means for clinical practice?

Dr. Sakoulas Thank you. I’m an infectious disease physician. I’ve been treating patients with viral acute respiratory distress syndrome for almost 20 years as an ID doctor. Most of these cases are due to influenza or other viruses. And from time to time, anecdotally and supported by some literature, we’ve been using IVIG, or intravenous immunoglobulin, in some of these cases. And again, I can report anecdotal success with that over the years.

So when COVID emerged in March of 2020, we deployed IVIG in a couple of patients early who were heading downhill. Remember, in March of 2020, we didn’t have the knowledge of steroids helping, patients being ventilated very promptly, and we saw some patients who made a turnaround after treatment with IVIG. We were able to get some support from an industry sponsor and perform and publish a pilot study, enrolling patients early in the pandemic. That study actually showed benefits, which then led the sponsor to fund a phase 3 multicenter clinical trial. Unfortunately, a couple of things happened. First, the trial was designed with the knowledge we had in April of 2020, and again, this is before steroids, before we incorporated proning patients in the ICU, or started ventilating people early. So there were some management changes and evolutions and improvements that happened. And second, the trial was enrolling a very broad repertoire of patients. There were no age limitations, and the trial, ultimately a phase 3 multicenter trial, failed to meet its endpoint.

There were some trends for benefit in younger patients, and as the trial was ongoing, we continued to evolve our knowledge, and we really honed it down to seeing a benefit of using IVIG in patients with COVID with specific criteria in mind. They had to be relatively younger patients, under 65, and not have any major comorbidities. In other words, they weren’t dialysis patients or end-stage disease patients, heart failure patients, cancer or malignancy patients. So, you know, we’re looking at the patients under 65 with obesity, diabetes, and hypertension, who are rapidly declining, going from room air to BiPAP or high-flow oxygen in a short amount of time. And we learned that when using IVIG early, we actually saw patients improve and turn around.

What this article in JCOM highlighted was, number one, incorporating that outcome or that patient type and then looking at the cost of hospitalization of patients who received IVIG versus those that did not. There were 2 groups that were studied. One was the group of patients in that original pilot trial that I discussed who were randomized to receive 1 or the other prospectively; it was an unblinded randomized study. And the second group was a matched case-control study where we had patients treated with IVIG matched by age and comorbidity status and level of hypoxia to patients that did not receive IVIG. We saw a financial benefit in shortening or reducing hospitalizations, really coming down to getting rid of that 20% tail of patients that wound up going to the ICU, getting intubated, and using a high amount of hospital resources that would ramp up the cost of hospitalization. We saw great mitigation of that with IVIG, and even with a small subset of patients, we were able to show a benefit.

Dr. Barkoudah Any thoughts on where we can implement the new findings from your article in our practice at the moment, knowing we now have practice guidelines and protocols to treat COVID-19? There was a tangible benefit in treating the patients the way you approached it in your important work. Could you share with us what would be implementable at the moment?

Dr. Sakoulas I think, fortunately, with the increasing host immunity in the population and decreased virulence of the virus, perhaps we won’t see as many patients of the type that were in these trials going forward, but I suspect we will perhaps in the unvaccinated patients that remain. I believe one-third of the United States is not vaccinated. So there is certainly a vulnerable group of people out there. Potentially, an unvaccinated patient who winds up getting very sick, the patient who is relatively young—what I’m looking at is the 30- to 65-year-old obese, hypertensive, or diabetic patient who comes in and, despite the steroids and the antivirals, rapidly deteriorates into requiring high-flow oxygen. I think implementing IVIG in that patient type would be helpful. I don’t think it’s going to be as helpful in patients who are very elderly, because I think the mechanism of the disease is different in an 80-year-old versus a 50-year-old patient. So again, hopefully, it will not amount to a lot of patients, but I still suspect hospitals are going to see, perhaps in the fall, when they’re expecting a greater number of cases, a trickling of patients that do meet the criteria that I described.

Dr. Barkoudah JCOM’s audience are the QI implementers and hospital leadership. And what caught my eye in your article is your perspective on the pharmacoeconomics of treating COVID-19, and I really appreciate your looking at the cost aspect. Would you talk about the economics of inpatient care, the total care that we provide now that we’re in the age of tocilizumab, and the current state of multiple layers of therapy?

Dr. Sakoulas The reason to look at the economics of it is because IVIG—which is actually not a drug, it’s a blood product—is very expensive. So, we received a considerable amount of administrative pushback implementing this treatment at the beginning outside of the clinical trial setting because it hadn’t been studied on a large scale and because the cost was so high, even though, as a clinician at the bedside, I was seeing a benefit in patients. This study came out of my trying to demonstrate to the folks that are keeping the economics of medicine in mind that, in fact, investing several thousand dollars of treatment in IVIG will save you cost of care, the cost of an ICU bed, the cost of a ventilator, and the cost even of ECMO, which is hugely expensive.

If you look at the numbers in the study, for two-thirds or three-quarters of the patients, your cost of care is actually greater than the controls because you’re giving them IVIG, and it’s increasing the cost of their care, even though three-quarters of the patients are going to do just as well without it. It’s that 20% to 25% of patients that really are going to benefit from it, where you’re reducing your cost of care so much, and you’re getting rid of that very, very expensive 20%, that there’s a cost savings across the board per patient. So, it’s hard to understand when you say you’re losing money on three-quarters of the patients, you’re only saving money on a quarter of the patients, but that cost of saving on that small subset is so substantial it’s really impacting all numbers.

Also, abandoning the outlier principle is sort of an underlying theme in how we think of things. We tend to ignore outliers, not consider them, but I think we really have to pay attention to the more extreme cases because those patients are the ones that drive not just the financial cost of care. Remember, if you’re down to 1 ventilator and you can cut down the use of scarce ICU resources, the cost is sort of even beyond the cost of money. It’s the cost of resources that may become scarce in some settings. So, I think it speaks to that as well.

A lot of the drugs that we use, for example, tocilizumab, were able to be studied in thousands of patients. If you look at the absolute numbers, the benefit of tocilizumab from a magnitude standpoint—low to mid twenties to high twenties—you know, reducing mortality from 29% to 24%. I mean, just take a step back and think about that. Even though it’s statistically significant, try telling a patient, “Well, I’m going to give you this treatment that’s going to reduce mortality from 29% to 24%.” You know, that doesn’t really change anything from a clinical significance standpoint. But they have a P value less than .05, which is our standard, and they were able to do a study with thousands of patients. We didn’t have that luxury with IVIG. No one studied thousands of patients, only retrospectively, and those retrospective studies don’t get the attention because they’re considered biased with all their limitations. But I think one of the difficulties we have here is the balance between statistical and clinical significance. For example, in our pilot study, our ventilation rate was 58% with the non-IVIG patients versus 14% for IVIG patients. So you might say, magnitude-wise, that’s a big number, but the statistical significance of it is borderline because of small numbers.

Anyway, that’s a challenge that we have as clinicians trying to incorporate what’s published—the balancing of statistics, absolute numbers, and practicalities of delivering care. And I think this study highlights some of the nuances that go into that incorporation and those clinical decisions.

Dr. Barkoudah Would you mind sharing with our audience how we can make the connection between the medical outcomes and pharmacoeconomics findings from your article and link it to the bedside and treatment of our patients?

Dr. Sakoulas One of the points this article brings out is the importance of bringing together not just level 1A data, but also small studies with data such as this, where the magnitude of the effect is pretty big but you lose the statistics because of the small numbers. And then also the patients’ aspects of things. I think, as a bedside clinician, you appreciate things, the nuances, much sooner than what percolates out from a level 1A study. Case in point, in the sponsored phase 3 study that we did, and in some other studies that were prospectively done as well, these studies of IVIG simply had an enrollment of patients that was very broad, and not every patient benefits from the same therapy. A great example of this is the sepsis trials with Xigris and those types of agents that failed. You know, there are clinicians to this day who believe that there is a subset of patients that benefit from agents like this. The IVIG story falls a little bit into that category. It comes down to trying to identify the subset of patients that might benefit. And I think we’ve outlined this subset pretty well in our study: the younger, obese diabetic or hypertensive patient who’s rapidly declining.

It really brings together the need to not necessarily toss out these smaller studies, but kind of summarize everything together, and clinicians who are bedside, who are more in tune with the nuances of individual decisions at the individual patient level, might better appreciate these kinds of data. But I think we all have to put it together. IVIG does not make treatment guidelines at national levels and so forth. It’s not even listed in many of them. But there are patients out there who, if you ask them specifically how they felt, including a friend of mine who received the medication, there’s no question from their end, how they felt about this treatment option. Now, some people will get it and will not benefit. We just have to be really tuned into the fact that the same drug does not have the same result for every patient. And just to consider this in the high-risk patients that we talked about in our study.

Dr. Barkoudah While we were prepping for this interview, you made an analogy regarding clinical evidence along the lines of, “Do we need randomized clinical trials to do a parachute-type of experiment,” and we chatted about clinical wisdom. Would you mind sharing with our readers your thoughts on that?

Dr. Sakoulas Sometimes, we try a treatment and it’s very obvious for that particular patient that it helped them. Then you study the treatment in a large trial setting and it doesn’t work. For us bedside clinicians, there are some interventions sometimes that do appear as beneficial as a parachute would be, but yet, there has never been a randomized clinical trial proving that parachutes work. Again, a part of the challenge we have is patients are so different, their immunology is different, the pathogen infecting them is different, the time they present is different. Some present early, some present late. There are just so many moving parts to treating an infection that only a subset of people are going to benefit. And sometimes as clinicians, we’re so nuanced, that we identify a specific subset of patients where we know we can help them. And it’s so obvious for us, like a parachute would be, but to people who are looking at the world from 30,000 feet, they don’t necessarily grasp that because, when you look at all comers, it doesn’t show a benefit.

So the problem is that now those treatments that might help a subset of patients are being denied, and the subset of patients that are going to benefit never get the treatment. Now we have to balance that with a lot of stuff that went on during the pandemic with, you know, ivermectin, hydroxychloroquine, and people pushing those things. Someone asked me once what I thought about hydroxychloroquine, and I said, “Well, somebody in the lab probably showed that it was beneficial, analogous to lighting tissue paper on fire on a plate and taking a cup of water and putting the fire out. Well, now, if you take that cup of water to the Caldor fire that’s burning in California on thousands of acres, you’re not going to be able to put the fire out with that cup of water.” So while it might work in the lab, it’s truly not going to work in a clinical setting. We have to balance individualizing care for patients with some information people are pushing out there that may not be necessarily translatable to the clinical setting.

I think there’s nothing better than being at the bedside, though, and being able to implement something and seeing what works. And really, experience goes a long way in being able to individually treat a patient optimally.

Dr. Barkoudah Thank you for everything you do at the bedside and your work on improving the treatment we have and how we can leverage knowledge to treat our patients. Thank you very much for your time and your scholarly contribution. We appreciate it and I hope the work will continue. We will keep working on treating COVID-19 patients with the best knowledge we have.

Q&A participants: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, La Jolla, CA, and University of California San Diego School of Medicine, San Diego, CA; and Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

Disclosures: None reported.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

From Memora Health (Dr. Flyckt and Dr. Colbert), San Francisco, CA; and Harvard Medical School (Dr. Colbert), Boston, MA.

A close relative was recently diagnosed with follicular lymphoma. He was cared for at a high-ranked cancer center by physicians with demonstrated expertise, and even had the support of a care navigator. Still, he was often left feeling overwhelmed and confused, holding an inch-thick stack of papers, instructions, and pamphlets. As he left his treatment planning visit, reeling from the emotional burden of his diagnosis and all the unfamiliar terminology, he didn’t know what to do or what to expect. Later, when he experienced early signs of tumor lysis syndrome, he struggled to reach his care team for triage and guidance. When he went to the emergency room, his oncologist was never informed.

This scenario is unfortunately common, and versions of this scenario play out thousands of times each day across the US health system. Within the clinic and hospital setting, patients receive excellent care from their providers, but a disconnect emerges once the patient leaves these medical settings: patients at home struggle to find guidance and support, while care teams lack the tools to engage patients between visits or monitor their health across care settings, providers, or episodes of care.

Leveraging Technology to Move From Episodes of Care to Complex Care Journeys

The use of automated messaging, artificial intelligence and natural language processing–driven chat experiences, and text-based support is becoming more common. However, health care lags behind other industries in the adoption of these technologies.^1,2 The slow pace can be warranted, given that health care is more complicated and higher risk than inquiring about a lost package, ordering groceries, or applying for a mortgage. At the same time, many of the consumer engagement tools used to guide an applicant through the multiple steps and complexities of their home loan process or to prompt viewers to select new shows to binge have applications in health care.

Over the past few years, technologies have emerged that guide patients through complex care journeys and allow care teams to monitor and engage patients between visits. These solutions come in different formats, but generally patients can receive messages on their phones that contain disease-specific educational content, prompts to fill prescriptions and take medications, and reminders and guidance on how to prepare for appointments and procedures. These programs also collect relevant data from patients through survey and electronic patient-reported outcomes instruments, as well as connected patient monitoring devices, that help track patient progress and identify issues as they arise. Many programs also incorporate symptom triage pathways and use natural language processing to respond automatically to patient questions and concerns.^3,4

These technology solutions can automate many tasks that in the past required a care team member to spend hours on the phone. Newly freed from such repetitive tasks, care teams can now focus on more in-depth interactions with those patients who are most in need—the types of interactions that are more satisfying and rewarding. Such assistance is particularly needed today with the staffing shortages faced by most health systems.⁵

In addition, technology allows teams to see the panel of patients they are caring for and to quickly identify and take action on any specific needs or issues. Care teams can focus on any patient and see where they are in their journey. When appropriate, some solutions also allow care teams to engage directly with patients through text-messaging, creating a seamless experience and unified communication channel. Ideally, these solutions should be linked or embedded within the electronic health record or other primary system of record, so that teams can easily access these tools through their existing workflows and avoid creating yet another interface to navigate.

The Impact of Low-Tech Solutions to Deliver High-Touch Support

There is evidence showing that digital patient navigation tools impact patient care. In the oncology setting, patients with a digital navigator have achieved over 95% adherence rates with complex oral chemotherapy regimens (Memora Health Unpublished Data. 2022.). In the postpartum setting, a text message–based program improved screening rates for postpartum depression and did so with very high patient satisfaction ratings.⁶ Particularly notable is the fact that this depression screening program achieved these results in a population that was predominantly low income, with more than half belonging to underrepresented minority populations.⁶

We believe these digital patient navigation technologies, specifically low-tech solutions that don’t require app downloads, portal log-ins, or high-speed internet, will transform care delivery over the next 5 to 10 years. Successful management of complex conditions like diabetes or cancer requires more than 3 hours of care each day,⁷ yet most patients spend only 1 or 2 hours per month directly interacting with their health care providers. However, most patients carry their phones with them at all times, and artificial intelligence–enabled text support is “always on” to provide support, monitoring, and guidance, wherever a patient happens to be when assistance is needed.

Shifting the Model to Support a Lifetime of Care

While still in the early stages of development, these tools have the potential to radically alter the practice of medicine, shifting the focus from episodic interactions to continuous journey-based care delivery. Outside of an acute event bringing a patient into the clinic or emergency room, many patients go a year or more without seeing their primary care providers.⁸ During that time, an immense amount of information is underreported or completely lost. Capturing this information in real-time and more holistically over a person’s lifetime of care could provide physicians better insight to both better manage and more fully evaluate the success of treatment plans by tracking patient symptoms, pain, and functional status over time. With this more longitudinal view of the patient, we see a pathway towards achieving the Quadruple Aim: patients who are more supported will achieve better outcomes at lower cost, they will have a better experience, and care teams will be empowered to focus their time on more satisfying activities rather than repetitive administrative tasks.

Corresponding author: James A. Colbert, MD, MBA; jamie@memorahealth.com

Disclosures: Dr. Flyckt and Dr. Colbert are employed by Memora Health, an organization that helps health care systems digitize and automate care journeys.

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

Study 1 Overview (Cortés et al)

Objective: To compare the efficacy and safety of trastuzumab deruxtecan with those of trastuzumab emtansine in patients with HER2-positive metastatic breast cancer previously treated with trastuzumab and taxane.

Design: Phase 3, multicenter, open-label randomized trial conducted at 169 centers and 15 countries.

Setting and participants: Eligible patients had to have unresectable or metastatic HER2-positive breast cancer that had progressed during or after treatment with trastuzumab and a taxane or had disease that progressed within 6 months after neoadjuvant or adjuvant treatment involving trastuzumab or taxane. Patients with stable or previously treated brain metastases were eligible. Patients were not eligible for the study if they had symptomatic brain metastases, prior exposure to trastuzumab emtansine, or a history of interstitial lung disease.

Intervention: Patients were randomized in a 1-to-1 fashion to receive either trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or trastuzumab emtansine 3.6 mg/kg every 3 weeks. Patients were stratified according to hormone-receptor status, prior treatment with epratuzumab, and the presence or absence of visceral disease.

Main outcome measures: The primary endpoint of the study was progression-free survival as determined by an independent central review. Secondary endpoints included overall survival, overall response, and safety.

Main results: A total of 524 patients were enrolled in the study, with 261 patients randomized to trastuzumab deruxtecan and 263 patients randomized to trastuzumab emtansine. The demographic and baseline characteristics were similar between the 2 cohorts, and 60% of patients in both groups received prior epratuzumab therapy. Stable brain metastases were present in around 20% of patients in each group, and 70% of patients in each group had visceral disease. The median duration of follow-up was 16.2 months with trastuzumab deruxtecan and 15.3 months with trastuzumab emtansine.

The median progression-free survival was not reached in the trastuzumab deruxtecan group and was 6.8 months in the trastuzumab emtansine group (95% CI, 5.6-8.2). At 12 months the percentage of patients alive without disease progression was significantly larger in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. The hazard ratio for disease progression or death from any cause was 0.28 (95% CI, 0.22-0.37; P < .001). Subgroup analyses showed a benefit in progression-free survival with trastuzumab deruxtecan across all subgroups.

At the time of this analysis, the percentage of patients who were alive at 12 months was 94% with trastuzumab deruxtecan and 85.9% with trastuzumab emtansine. The response rates were significantly higher with trastuzumab deruxtecan compared with trastuzumab emtansine (79.7% vs 34.2%). A complete response was seen in 16% of patients in the trastuzumab deruxtecan arm, compared with 8.7% of patients in the trastuzumab emtansine group. The disease control rate (complete response, partial response, or stable disease) was higher in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group (96.6% vs 76.8%).

Serious adverse events were reported in 19% of patients in the trastuzumab deruxtecan group and 18% of patients in the trastuzumab emtansine group. Discontinuation due to adverse events was higher in the trastuzumab deruxtecan group, with 13.6% of patients discontinuing trastuzumab deruxtecan. Grade 3 or higher adverse events were seen in 52% of patients treated with trastuzumab deruxtecan and 48% of patients treated with trastuzumab emtansine. The most commonly reported adverse event with trastuzumab deruxtecan was nausea/vomiting and fatigue. These adverse events were seen more in the trastuzumab deruxtecan group compared with the trastuzumab emtansine group. No drug-related grade 5 adverse events were reported.

In the trastuzumab deruxtecan group, 10.5% of patients receiving trastuzumab deruxtecan developed interstitial lung disease or pneumonitis. Seven patients had grade 1 events, 18 patients had grade 2 events, and 2 patients had grade 3 events. No grade 4 or 5 events were noted in either treatment group. The median time to onset of interstitial lung disease or pneumonitis in those receiving trastuzumab deruxtecan was 168 days (range, 33-507). Discontinuation of therapy due to interstitial lung disease or pneumonitis occurred in 8% of patients receiving trastuzumab deruxtecan and 1% of patients receiving trastuzumab emtansine.

Conclusion: Trastuzumab deruxtecan significantly decreases the risk of disease progression or death compared to trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on prior trastuzumab and taxane-based therapy.

Study 2 Overview (Modi et al)

Objective: To assess the efficacy of trastuzumab deruxtecan in patients with unresectable or metastatic breast cancer with low levels of HER2 expression.

Design: This was a randomized, 2-group, open-label, phase 3 trial.

Setting and participants: The trial was designed with a planned enrollment of 480 patients with hormone receptor–positive disease and 60 patients with hormone receptor–negative disease. Patients were randomized in a 2:1 ratio. Randomization was stratified according to HER2 status (immunohistochemical [IHC] 1+ vs IHC 2+/in situ hybridization [ISH] negative), number of prior lines of therapy, and hormone-receptor status. IHC scores for HER2 expression were determined through central testing. Specimens that had HER2 IHC scores of 2+ were reflexed to ISH. Specimens were considered HER2-low-expressing if they had an IHC score of 1+ or if they had an IHC score of 2+ and were ISH negative.

Eligible patients had to have received chemotherapy for metastatic disease or had disease recurrence during or within 6 months after completing adjuvant chemotherapy. Patients with hormone receptor–positive disease must have had at least 1 line of endocrine therapy. Patients were eligible if they had stable brain metastases. Patients with interstitial lung disease were excluded.

Intervention: Patients were randomized to receive trastuzumab deruxtecan 5.4 mg/kg every 3 weeks or physician’s choice of chemotherapy (capecitabine, eribulin, gemcitabine, paclitaxel, or nab-paclitaxel).

Main outcome measures: The primary endpoint was progression-free survival in patients with hormone receptor–positive disease. Secondary endpoints were progression-free survival among all patients, overall survival in hormone receptor–positive patients, and overall survival in all patients. Additional secondary endpoints included objective response rates, duration of response, and efficacy in hormone receptor–negative patients.

Main results: A total of 373 patients were assigned to the trastuzumab deruxtecan group and 184 patients were assigned to the physician’s choice chemotherapy group; 88% of patients in each cohort were hormone receptor–positive. In the physician’s choice chemotherapy group, 51% received eribulin, 20% received capecitabine, 10% received nab-paclitaxel, 10% received gemcitabine, and 8% received paclitaxel. The demographic and baseline characteristics were similar between both cohorts. The median duration of follow-up was 18.4 months.

The median progression-free survival in the hormone receptor–positive cohort was 10.1 months in the trastuzumab deruxtecan group and 5.4 months in the physician’s choice chemotherapy group (HR, 0.51; 95% CI, 0.4-0.64). Subgroup analyses revealed a benefit across all subgroups. The median progression-free survival among patients with a HER2 IHC score of 1+ and those with a HER2 IHC score of 2+/negative ISH were identical. In patients who received a prior CDK 4/6 inhibitor, the median progression-free survival was also 10 months in the trastuzumab deruxtecan group. In those who were CDK 4/6- naïve, the progression-free survival was 11.7 months. The progression-free survival in all patients was 9.9 months in the trastuzumab deruxtecan group and 5.1 months in the physician’s choice chemotherapy group (HR, 0.46; 95% CI, 0.24-0.89).

The median overall survival in the hormone receptor–positive cohort was 23.9 months in the trastuzumab deruxtecan group compared with 17.5 months in the physician’s choice chemotherapy group (HR, 0.64; 95% CI, 0.48-0.86; P = .003). The median overall survival in the entire population was 23.4 months in the trastuzumab deruxtecan group vs 16.8 months in the physician’s choice chemotherapy group. In the hormone receptor–negative cohort, the median overall survival was 18.2 months in the trastuzumab deruxtecan group and 8.3 months in the physician’s choice chemotherapy group. Complete responses were seen in 3.6% in the trastuzumab deruxtecan group and 0.6% and the physician’s choice chemotherapy group. The median duration of response was 10.7 months in the trastuzumab deruxtecan group and 6.8 months in the physician’s choice chemotherapy group.

Incidence of serious adverse events was 27% in the trastuzumab deruxtecan group and 25% in the physician’s choice chemotherapy group. Grade 3 or higher events occurred in 52% of the trastuzumab deruxtecan group and 67% of the physician’s choice chemotherapy group. Discontinuation due to adverse events occurred in 16% in the trastuzumab deruxtecan group and 18% in the physician’s choice chemotherapy group; 14 patients in the trastuzumab deruxtecan group and 5 patients in the physician’s choice chemotherapy group had an adverse event that was associated with death. Death due to pneumonitis in the trastuzumab deruxtecan group occurred in 2 patients. Drug-related interstitial lung disease or pneumonitis occurred in 45 patients who received trastuzumab deruxtecan. The majority of these events were grade 1 and grade 2. However, 3 patients had grade 5 interstitial lung disease or pneumonitis.

Conclusion: Treatment with trastuzumab deruxtecan led to a significant improvement in progression-free survival compared to physician’s choice chemotherapy in patients with HER2-low metastatic breast cancer.

Commentary

Trastuzumab deruxtecan is an antibody drug conjugate that consists of a humanized anti-HER2 monoclonal antibody linked to a topoisomerase 1 inhibitor. This antibody drug conjugate is unique compared with prior antibody drug conjugates such as trastuzumab emtansine in that it has a high drug-to-antibody ratio (~8). Furthermore, there appears to be a unique bystander effect resulting in off-target cytotoxicity to neighboring tumor cells, enhancing the efficacy of this novel therapy. Prior studies of trastuzumab deruxtecan have shown durable activity in heavily pretreated patients with metastatic HER2-positive breast cancer.¹

HER2-positive breast cancer represents approximately 20% of breast cancer cases in women.² Historically, HER2 positivity has been defined by strong HER2 expression with IHC staining (ie, score 3+) or HER2 amplification through ISH. Conversely, HER2-negative disease has historically been defined as those with IHC scores of 0 or 1+. This group represents approximately 60% of HER2-negative metastatic breast cancer patients.³ These patients have limited targeted treatment options after progressing on primary therapy. Prior data has shown that patients with low HER2 expression represent a heterogeneous population and thus, the historic categorization of HER2 status as positive or negative may in fact not adequately characterize the proportion of patients who may derive clinical benefit from HER2-directed therapies. Nevertheless, there have been no data to date that have shown improved outcomes in low HER2 expressers with anti-HER2 therapies.

The current studies add to the rapidly growing body of literature outlining the efficacy of the novel antibody drug conjugate trastuzumab deruxtecan. The implications of the data presented in these 2 studies are immediately practice changing.

In the DESTINY-Breast03 trial, Cortéz and colleagues show that trastuzumab deruxtecan therapy significantly prolongs progression-free survival compared with trastuzumab emtansine in patients with HER2-positive metastatic breast cancer who have progressed on first-line trastuzumab and taxane-based therapy. With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in this trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer. The overall survival in this trial was immature at the time of this analysis, and thus continued follow-up to validate the results noted here are warranted.

The DESTINY-Breast04 trial by Modi et al expands the cohort of patients who benefit from trastuzumab deruxtecan profoundly. This study defines a population of patients with HER2-low metastatic breast cancer who will now be eligible for HER2-directed therapies. These data show that therapy with trastuzumab deruxtecan leads to a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy in patients with metastatic breast cancer with low expression of HER2. This benefit was seen in both the estrogen receptor–positive cohort as well as the entire population, including pre-treated triple-negative disease. Furthermore, this study does not define a threshold of HER2 expression by IHC that predicts benefit with trastuzumab deruxtecan. Patients with an IHC score of 1+ as well as those with a score of 2+/ISH negative both benefit to a similar extent from trastuzumab deruxtecan. Interestingly, in the DAISY trial, antitumor activity was noted with trastuzumab deruxtecan even in those without any detectable HER2 expression on IHC.⁴ Given the inconsistency and potential false negatives of IHC along with heterogeneous HER2 expression, further work is needed to better identify patients with low levels of HER2 expression who may benefit from this novel antibody drug conjugate. Thus, a reliable test to quantitatively assess the level of HER2 expression is needed in order to determine more accurately which patients will benefit from trastuzumab deruxtecan.

Last, trastuzumab deruxtecan has been associated with interstitial lung disease and pneumonitis. Interstitial lung disease and pneumonitis occurred in approximately 10% of patients who received trastuzumab deruxtecan in the DESTINY-Breast03 trial and about 12% of patients in the DESTINY-Breast04 trial. Most of these events were grade 1 and grade 2. Nevertheless, clinicians must be aware of this risk and monitor patients frequently for the development of pneumonitis or interstitial lung disease.

Application for Clinical Practice and System Implementation

The results of the current studies show a longer progression-free survival with trastuzumab deruxtecan in both HER2-low expressing metastatic breast cancer and HER2-positive metastatic breast cancer following taxane and trastuzumab-based therapy. These results are clearly practice changing and represent a new standard of care in these patient populations. It is incumbent upon treating oncologists to work with our pathology colleagues to assess HER2 IHC thoroughly in order to identify all potential patients who may benefit from trastuzumab deruxtecan in the metastatic setting. The continued advancement of anti-HER2 therapy will undoubtedly have a significant impact on patient outcomes going forward.

Practice Points

• With a hazard ratio of 0.28 for disease progression or death, the efficacy of trastuzumab deruxtecan highlighted in the DESTINY-Breast03 trial clearly makes this the standard of care in the second-line setting for patients with metastatic HER2-positive breast cancer.
• In the DESTINY-Breast04 trial, a significant and clinically meaningful improvement in both progression-free survival and overall survival compared with chemotherapy was seen in patients with metastatic breast cancer with low expression of HER2, including both the estrogen receptor–positive cohort as well as the entire population, including those with pre-treated triple-negative disease.

­—Daniel Isaac, DO, MS

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

From Sharp Memorial Hospital, San Diego, CA (Drs. Poremba, Dehner, Perreiter, Semma, and Mills), Sharp Rees-Stealy Medical Group, San Diego, CA (Dr. Sakoulas), and Collaborative to Halt Antibiotic-Resistant Microbes (CHARM), Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA (Dr. Sakoulas).

Abstract

Objective: To compare the costs of hospitalization of patients with moderate-to-severe COVID-19 who received intravenous immunoglobulin (IVIG) with those of patients of similar comorbidity and illness severity who did not.

Design: Analysis 1 was a case-control study of 10 nonventilated, moderately to severely hypoxic patients with COVID-19 who received IVIG (Privigen [CSL Behring]) matched 1:2 with 20 control patients of similar age, body mass index, degree of hypoxemia, and comorbidities. Analysis 2 consisted of patients enrolled in a previously published, randomized, open-label prospective study of 14 patients with COVID-19 receiving standard of care vs 13 patients who received standard of care plus IVIG (Octagam 10% [Octapharma]).

Setting and participants: Patients with COVID-19 with moderate-to-severe hypoxemia hospitalized at a single site located in San Diego, California.

Measurements: Direct cost of hospitalization.

Results: In the first (case-control) population, mean total direct costs, including IVIG, for the treatment group were $21,982 per IVIG-treated case vs $42,431 per case for matched non-IVIG-receiving controls, representing a net cost reduction of $20,449 (48%) per case. For the second (randomized) group, mean total direct costs, including IVIG, for the treatment group were $28,268 per case vs $62,707 per case for untreated controls, representing a net cost reduction of $34,439 (55%) per case. Of the patients who did not receive IVIG, 24% had hospital costs exceeding $80,000; none of the IVIG-treated patients had costs exceeding this amount (P = .016, Fisher exact test).

Conclusion: If allocated early to the appropriate patient type (moderate-to-severe illness without end-organ comorbidities and age <70 years), IVIG can significantly reduce hospital costs in COVID-19 care. More important, in our study it reduced the demand for scarce critical care resources during the COVID-19 pandemic.

Keywords: IVIG, SARS-CoV-2, cost saving, direct hospital costs.

Intravenous immunoglobulin (IVIG) has been available in most hospitals for 4 decades, with broad therapeutic applications in the treatment of Kawasaki disease and a variety of inflammatory, infectious, autoimmune, and viral diseases, via multifactorial mechanisms of immune modulation.¹ Reports of COVID-19−associated multisystem inflammatory syndrome in adults and children have supported the use of IVIG in treatment.^2,3 Previous studies of IVIG treatment for COVID-19 have produced mixed results. Although retrospective studies have largely been positive,^4-8 prospective clinical trials have been mixed, with some favorable results^9-11 and another, more recent study showing no benefit.¹² However, there is still considerable debate regarding whether some subgroups of patients with COVID-19 may benefit from IVIG; the studies that support this argument, however, have been diluted by broad clinical trials that lack granularity among the heterogeneity of patient characteristics and the timing of IVIG administration.^13,14 One study suggests that patients with COVID-19 who may be particularly poised to benefit from IVIG are those who are younger, have fewer comorbidities, and are treated early.⁸

At our institution, we selectively utilized IVIG to treat patients within 48 hours of rapidly increasing oxygen requirements due to COVID-19, targeting those younger than 70 years, with no previous irreversible end-organ damage, no significant comorbidities (renal failure, heart failure, dementia, active cancer malignancies), and no active treatment for cancer. We analyzed the costs of care of these IVIG (Privigen) recipients and compared them to costs for patients with COVID-19 matched by comorbidities, age, and illness severity who did not receive IVIG. To look for consistency, we examined the cost of care of COVID-19 patients who received IVIG (Octagam) as compared to controls from a previously published pilot trial.¹⁰

Methods

Setting and Treatment

All patients in this study were hospitalized at a single site located in San Diego, California. Treatment patients in both cohorts received IVIG 0.5 g/kg adjusted for body weight daily for 3 consecutive days.

Patient Cohort #1: Retrospective Case-Control Trial

Intravenous immunoglobulin (Privigen 10%, CSL Behring) was utilized off-label to treat moderately to severely ill non-intensive care unit (ICU) patients with COVID-19 requiring ≥3 L of oxygen by nasal cannula who were not mechanically ventilated but were considered at high risk for respiratory failure. Preset exclusion criteria for off-label use of IVIG in the treatment of COVID-19 were age >70 years, active malignancy, organ transplant recipient, renal failure, heart failure, or dementia. Controls were obtained from a list of all admitted patients with COVID-19, matched to cases 2:1 on the basis of age (±10 years), body mass index (±1), gender, comorbidities present at admission (eg, hypertension, diabetes mellitus, lung disease, or history of tobacco use), and maximum oxygen requirements within the first 48 hours of admission. In situations where more than 2 potential matched controls were identified for a patient, the 2 controls closest in age to the treatment patient were selected. One IVIG patient was excluded because only 1 matched-age control could be found. Pregnant patients who otherwise fulfilled the criteria for IVIG administration were also excluded from this analysis.

Patient Cohort #2: Prospective, Randomized, Open-Label Trial

Use of IVIG (Octagam 10%, Octapharma) in COVID-19 was studied in a previously published, prospective, open-label randomized trial.¹⁰ This pilot trial included 16 IVIG-treated patients and 17 control patients, of which 13 and 14 patients, respectively, had hospital cost data available for analysis.¹⁰ Most notably, COVID-19 patients in this study were required to have ≥4 L of oxygen via nasal cannula to maintain arterial oxygen saturationof ≤96%.

Outcomes

Cost data were independently obtained from our finance team, which provided us with the total direct cost and the total pharmaceutical cost associated with each admission. We also compared total length of stay (LOS) and ICU LOS between treatment arms, as these were presumed to be the major drivers of cost difference.

Statistics

Nonparametric comparisons of medians were performed with the Mann-Whitney U test. Comparison of means was done by Student t test. Categorical data were analyzed by Fisher exact test.

This analysis was initiated as an internal quality assessment. It received approval from the Sharp Healthcare Institutional Review Board (research@sharp.com), and was granted a waiver of subject authorization and consent given the retrospective nature of the study.

Results

Case-Control Analysis

A total of 10 hypoxic patients with COVID-19 received Privigen IVIG outside of clinical trial settings. None of the patients was vaccinated against SARS-CoV-2, as hospitalization occurred prior to vaccine availability. In addition, the original SARS-CoV-2 strain was circulating while these patients were hospitalized, preceding subsequent emerging variants. Oxygen requirements within the first 48 hours ranged from 3 L via nasal cannula to requiring bi-level positive pressure airway therapy with 100% oxygen; median age was 56 years and median Charlson comorbidity index was 1. These 10 patients were each matched to 2 control patients hospitalized during a comparable time period and who, based on oxygen requirements, did not receive IVIG. The 20 control patients had a median age of 58.5 years and a Charlson comorbidity index of 1 (Table 1). Rates of comorbidities, such as hypertension, diabetes mellitus, and obesity, were identical in the 2 groups. None of the patients in either group died during the index hospitalization. Fewer control patients received glucocorticoids, which was reflective of lower illness severity/degree of hypoxia in some controls.

Health care utilization in terms of costs and hospital LOS between the 2 groups are shown in Table 2. The mean total direct hospital cost per case, including IVIG and other drug costs, for the 10 IVIG-treated COVID-19 patients was $21,982 vs $42,431 for the matched controls, a reduction of $20,449 (48%) per case (P = .6187) with IVIG. This difference was heavily driven by 4 control patients (20%) with hospital costs >$80,000, marked by need for ICU transfer, mechanical ventilation during admission, and longer hospital stays. This reduction in progression to mechanical ventilation was consistent with our previously published, open-label, randomized prospective IVIG study, the financial assessment of which is reviewed below. While total direct costs were lower in the treatment arm, the mean drug cost for the treatment arm was $3122 greater than the mean drug cost in the control arm (P = .001622), consistent with the high cost of IVIG therapy (Table 2).

LOS information was obtained, as this was thought to be a primary driver of direct costs. The average LOS in the IVIG arm was 8.4 days, and the average LOS in the control arm was 13.6 days (P = NS). The average ICU LOS in the IVIG arm was 0 days, while the average ICU LOS in the control arm was 5.3 days (P = .04). As with the differences in cost, the differences in LOS were primarily driven by the 4 outlier cases in our control arm, who each had a LOS >25 days, as well as an ICU LOS >20 days.

Randomized, Open-Label, Patient Cohort Analysis

Patient characteristics, LOS, and rates of mechanical ventilation for the IVIG and control patients were previously published and showed a reduction in mechanical ventilation and hospital LOS with IVIG treatment.¹⁰ In this group of patients, 1 patient treated with IVIG (6%) and 3 patients not treated with IVIG (18%) died. To determine the consistency of these results from the case-control patients with a set of patients obtained from clinical trial randomization, we examined the health care costs of patients from the prior study.¹⁰ As with the case-control group, patients in this portion of the analysis were hospitalized before vaccines were available and prior to any identified variants.

Comparing the hospital cost of the IVIG-treated patients to the control patients from this trial revealed results similar to the matched case-control analysis discussed earlier. Average total direct cost per case, including IVIG, for the IVIG treatment group was $28,268, vs $62,707 per case for non-IVIG controls. This represented a net cost reduction of $34,439 (55%) per case, very similar to that of the prior cohort.

IVIG Reduces Costly Outlier Cases

The case-control and randomized trial groups, yielding a combined 23 IVIG and 34 control patients, showed a median cost per case of $22,578 (range $10,115-$70,929) and $22,645 (range $4723-$279,797) for the IVIG and control groups, respectively. Cases with a cost >$80,000 were 0/23 (0%) vs 8/34 (24%) in the IVIG and control groups, respectively (P = .016, Fisher exact test).

Improving care while simultaneously keeping care costs below reimbursement payment levels received from third-party payers is paramount to the financial survival of health care systems. IVIG appears to do this by reducing the number of patients with COVID-19 who progress to ICU care. We compared the costs of care of our combined case-control and randomized trial cohorts to published data on average reimbursements hospitals receive for COVID-19 care from Medicaid, Medicare, and private insurance (Figure).¹⁵ IVIG demonstrated a reduction in cases where costs exceed reimbursement. Indeed, a comparison of net revenue per case of the case-control group showed significantly higher revenue for the IVIG group compared to controls ($52,704 vs $34,712, P = .0338, Table 2).

Discussion

As reflected in at least 1 other study,¹⁶ our hospital had been successfully utilizing IVIG in the treatment of viral acute respiratory distress syndrome (ARDS) prior to COVID-19. Therefore, we moved quickly to perform a randomized, open-label pilot study of IVIG (Octagam 10%) in COVID-19, and noted significant clinical benefit that might translate into hospital cost savings.¹⁰ Over the course of the pandemic, evidence has accumulated that IVIG may play an important role in COVID-19 therapeutics, as summarized in a recent review.¹⁷ However, despite promising but inconsistent results, the relatively high acquisition costs of IVIG raised questions as to its pharmacoeconomic value, particularly with such a high volume of COVID-19 patients with hypoxia, in light of limited clinical data.

COVID-19 therapeutics data can be categorized into either high-quality trials showing marginal benefit for some agents or low-quality trials showing greater benefit for other agents, with IVIG studies falling into the latter category.¹⁸ This phenomenon may speak to the pathophysiological heterogeneity of the COVID-19 patient population. High-quality trials enrolling broad patient types lack the granularity to capture and single out relevant patient subsets who would derive maximal therapeutic benefit, with those subsets diluted by other patient types for which no benefit is seen. Meanwhile, the more granular low-quality trials are criticized as underpowered and lacking in translatability to practice.

Positive results from our pilot trial allowed the use of IVIG (Privigen) off-label in hospitalized COVID-19 patients restricted to specific criteria. Patients had to be moderately to severely ill, requiring >3 L of oxygen via nasal cannula; show high risk of clinical deterioration based on respiratory rate and decline in respiratory status; and have underlying comorbidities (such as hypertension, obesity, or diabetes mellitus). However, older patients (>age 70 years) and those with underlying comorbidities marked by organ failure (such as heart failure, renal failure, dementia, or receipt of organ transplant) and active malignancy were excluded, as their clinical outcome in COVID-19 may be considered less modifiable by therapeutics, while simultaneously carrying potentially a higher risk of adverse events from IVIG (volume overload, renal failure). These exclusions are reflected in the overall low Charlson comorbidity index (mean of 1) of the patients in the case-control study arm. As anticipated, we found a net cost reduction: $20,449 (48%) per case among the 10 IVIG-treated patients compared to the 20 matched controls.

We then went back to the patients from the randomized prospective trial and compared costs for the 13 of 16 IVIG patients and 14 of 17 of the control patients for whom data were available. Among untreated controls, we found a net cost reduction of $34,439 (55%) per case. The higher costs seen in the randomized patient cohort compared to the latter case-control group may be due to a combination of the fact that the treated patients had slightly higher comorbidity indices than the case-control group (median Charlson comorbidity index of 2 in both groups) and the fact that they were treated earlier in the pandemic (May/June 2020), as opposed to the case-control group patients, who were treated in November/December 2020.

It was notable that the cost savings across both groups were derived largely from the reduction in the approximately 20% to 25% of control patients who went on to critical illness, including mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and prolonged ICU stays. Indeed, 8 of 34 of the control patients—but none of the 23 IVIG-treated patients—generated hospital costs in excess of $80,000, a difference that was statistically significant even for such a small sample size. Therefore, reducing these very costly outlier events translated into net savings across the board.

In addition to lowering costs, reducing progression to critical illness is extremely important during heavy waves of COVID-19, when the sheer volume of patients results in severe strain due to the relative scarcity of ICU beds, mechanical ventilators, and ECMO. Therefore, reducing the need for these resources would have a vital role that cannot be measured economically.

The major limitations of this study include the small sample size and the potential lack of generalizability of these results to all hospital centers and treating providers. Our group has considerable experience in IVIG utilization in COVID-19 and, as a result, has identified a “sweet spot,” where benefits were seen clinically and economically. However, it remains to be determined whether IVIG will benefit patients with greater illness severity, such as those in the ICU, on mechanical ventilation, or ECMO. Furthermore, while a significant morbidity and mortality burden of COVID-19 rests in extremely elderly patients and those with end-organ comorbidities such as renal failure and heart failure, it is uncertain whether their COVID-19 adverse outcomes can be improved with IVIG or other therapies. We believe such patients may limit the pharmacoeconomic value of IVIG due to their generally poorer prognosis, regardless of intervention. On the other hand, COVID-19 patients who are not that severely ill, with minimal to no hypoxia, generally will do well regardless of therapy. Therefore, IVIG intervention may be an unnecessary treatment expense. Evidence for this was suggested in our pilot trial¹⁰ and supported in a recent meta-analysis of IVIG therapy in COVID-19.¹⁹

Several other therapeutic options with high acquisition costs have seen an increase in use during the COVID-19 pandemic despite relatively lukewarm data. Remdesivir, the first drug found to have a beneficial effect on hospitalized patients with COVID-19, is priced at $3120 for a complete 5-day treatment course in the United States. This was in line with initial pricing models from the Institute for Clinical and Economic Review (ICER) in May 2020, assuming a mortality benefit with remdesivir use. After the SOLIDARITY trial was published, which showed no mortality benefit associated with remdesivir, ICER updated their pricing models in June 2020 and released a statement that the price of remdesivir was too high to align with demonstrated benefits.^20,21 More recent data demonstrate that remdesivir may be beneficial, but only if administered to patients with fewer than 6 days of symptoms.²² However, only a minority of patients present to the hospital early enough in their illness for remdesivir to be beneficial.²²

Tocilizumab, an interleukin-6 inhibitor, saw an increase in use during the pandemic. An 800-mg treatment course for COVID-19 costs $3584. The efficacy of this treatment option came into question after the COVACTA trial failed to show a difference in clinical status or mortality in COVID-19 patients who received tocilizumab vs placebo.^23,24 A more recent study pointed to a survival benefit of tocilizumab in COVID-19, driven by a very large sample size (>4000), yielding statistically significant, but perhaps clinically less significant, effects on survival.²⁵ This latter study points to the extremely large sample sizes required to capture statistically significant benefits of expensive interventions in COVID-19, which our data demonstrate may benefit only a fraction of patients (20%-25% of patients in the case of IVIG). A more granular clinical assessment of these other interventions is needed to be able to capture the patient subtypes where tocilizumab, remdesivir, and other therapies will be cost effective in the treatment of COVID-19 or other virally mediated cases of ARDS.

Conclusion

While IVIG has a high acquisition cost, the drug’s use in hypoxic COVID-19 patients resulted in reduced costs per COVID-19 case of approximately 50% and use of less critical care resources. The difference was consistent between 2 cohorts (randomized trial vs off-label use in prespecified COVID-19 patient types), IVIG products used (Octagam 10% and Privigen), and time period in the pandemic (waves 1 and 2 in May/June 2020 vs wave 3 in November/December 2020), thereby adjusting for potential differences in circulating viral strains. Furthermore, patients from both groups predated SARS-CoV-2 vaccine availability and major circulating viral variants (eg, delta, omicron), thereby eliminating confounding on outcomes posed by these factors. Control patients’ higher costs of care were driven largely by the approximately 25% of patients who required costly hospital critical care resources, a group mitigated by IVIG. When allocated to the appropriate patient type (patients with moderate-to-severe but not critical illness, <age 70 without preexisting comorbidities of end-organ failure or active cancer), IVIG can reduce hospital costs for COVID-19 care. Identification of specific patient populations where IVIG has the most anticipated benefits in viral illness is needed.

Corresponding author: George Sakoulas, MD, Sharp Rees-Stealy Medical Group, 2020 Genesee Avenue, 2nd Floor, San Diego, CA 92123; gsakoulas@health.ucsd.edu

Disclosures: Dr Sakoulas has worked as a consultant for Abbvie, Paratek, and Octapharma, has served as a speaker for Abbvie and Paratek, and has received research funding from Octapharma. The other authors did not report any disclosures.

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI

Study Overview

Objective: To evaluate whether the addition of the potent androgen-receptor inhibitor (ARA) darolutamide to the standard doublet androgen-deprivation therapy (ADT) and docetaxel in metastatic, hormone-sensitive prostate cancer (mHSPC) would increase survival.

Design: A randomized, double-blind, placebo-controlled, multicenter, phase 3 study. The results reported in this publication are from the prespecified interim analysis.

Intervention: Patients with mHSPC were randomly assigned to receive either darolutamide 600 mg twice daily or placebo. All patients received standard ADT with 6 cycles of docetaxel 75 mg/m² on day 1 every 21 days along with prednisone given within 6 weeks after randomization. Patients receiving luteinizing hormone–releasing hormone (LHRH) agonists as ADT were bridged with at least 4 weeks of first-generation antiandrogen therapy, which was discontinued before randomization. Treatments were continued until symptomatic disease progression, a change in neoplastic therapy, unacceptable toxicity, patient or physician decision, death, or nonadherence.

Setting and participants: Eligible patients included those newly diagnosed with mHSPC with metastases detected on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) and bone scan. Patients were excluded if they had regional lymph node–only involvement or if they had received more than 12 weeks of ADT before randomization. Between November 2016 and June 2018, 1306 patients (651 in the darolutamide group and 655 in the placebo group) were randomized in a 1:1 manner to receive darolutamide 600 mg twice daily or placebo in addition to ADT and docetaxel. Randomization was stratified based on the TNM staging system (M1a—nonregional lymph node–only metastasis, M1b—bone metastasis with or without lymph node, or M1c—bone metastases) as well as baseline alkaline phosphatase levels.

Main outcome measures: The primary end point for the study was overall survival. Other meaningful secondary end points included time to castration resistance, time to pain progression, time to first symptomatic skeletal event, symptomatic skeletal event-free survival, time to subsequent systemic antineoplastic therapy, time to worsening of disease-related physical symptoms, initiation of opioid therapy for ≥7 days, and safety.

Results: The baseline and demographic characteristics were well balanced between the 2 groups. Median age was 67 years. Nearly 80% of patients had bone metastasis, and approximately 17% had visceral metastasis. At the data cutoff date for the primary analysis, the median duration of therapy was 41 months for darolutamide compared with 16.7 months in the placebo group; 45.9% in the darolutamide group and 19.1% in the placebo group were receiving the allotted trial therapy at the time of the analysis. Six cycles of docetaxel were completed in approximately 85% of patients in both arms. Median overall survival follow-up was 43.7 months (darolutamide) and 42.4 months (placebo). A significant improvement in overall survival was observed in the darolutamide group. The risk of death was 32.5% lower in the darolutamide cohort than in the placebo cohort (hazard ratio [HR], 0.68; 95% CI, 0.57-0.80; P < .001). The overall survival at 4 years was 62.7% (95% CI, 58.7-66.7) in the darolutamide arm and 50.4% (95% CI, 46.3-54.6) in the placebo arm. The overall survival results remained favorable across most subgroups.

Darolutamide was associated with improvement in all key secondary endpoints. Time to castration-resistance was significantly longer in the darolutamide group (HR, 0.36; 95% CI, 0.30-0.42; P < .001). Time to pain progression was also significantly longer in the darolutamide group (HR, 0.79; 95% CI, 0.66-0.95; P = .01). Time to first symptomatic skeletal events (HR, 0.71; 95% CI, 0.54-0.94; P = .02) and time to initiation of subsequent systemic therapy (HR, 0.39; 95% CI, 0.33-0.46; P < .001) were also found to be longer in the darolutamide group.

Safety: The risk of grade 3 or higher adverse events was similar across the 2 groups. Most common adverse events were known toxic effects of docetaxel therapy and were highest during the initial period when both groups received this therapy. These side effects progressively decreased after the initial period. The most common grade 3 or 4 adverse event was neutropenia, and its frequency was similar between the darolutamide and placebo groups (33.7% and 34.2%, respectively). The most frequently reported adverse events were alopecia, neutropenia, fatigue, and anemia and were similar between the groups. Adverse events of special significance, including fatigue, falls, fractures, and cardiovascular events, were also similar between the 2 groups. Adverse events causing deaths in each arm were low and similar (4.1% in the darolutamide group and 4.0% in the placebo group). The rates of discontinuation of darolutamide or placebo were similar (13.5% and 10.6%, respectively).

Conclusion: Among patients with mHSPC, overall survival was significantly longer among patients who received darolutamide plus ADT and docetaxel than among those who received ADT and docetaxel alone. This was observed despite a high percentage of patients in the placebo group receiving subsequent systemic therapy at the time of progression. The survival benefit of darolutamide was maintained across most subgroups. An improvement was also observed in the darolutamide arm in terms of key secondary end points. The adverse events were similar across the groups and were consistent with known safety profiles of ADT and docetaxel, and no new safety signals were identified in this trial.

Commentary

The results of the current study add to the body of literature supporting multi-agent systemic therapy in newly diagnosed mHSPC. Prior phase 3 trials of combination therapy using androgen-receptor pathway inhibitors, ADT, and docetaxel have shown conflicting results. The results from the previously reported PEACE-1 study showed improved overall survival among patients who received abiraterone with ADT and docetaxel as compared with those who received ADT and docetaxel alone.¹ However, as noted by the authors, the subgroup of patients in the ENZAMET trial who received docetaxel, enzalutamide, and ADT did not appear to have a survival advantage compared with those who received ADT and docetaxel alone.² The results from the current ARASENS trial provide compelling evidence in a population of prospectively randomized patients that combination therapy with darolutamide, docetaxel, and ADT improves overall survival in men with mHSPC. The survival advantage was maintained across subgroups analyzed in this study. Improvements were observed in regards to several key secondary end points with use of darolutamide. This benefit was maintained despite many patients receiving subsequent therapy at the time of progression. Importantly, there did not appear to be a significant increase in toxicity with triplet therapy. However, it is important to note that this cohort of patients appeared largely asymptomatic at the time of enrollment, with 70% of patients having an Eastern Cooperative Oncology Group performance status of 0.

Additionally, the average age in this study was 67 years, with only about 15% of the population being older than 75 years. In the reported subgroup analysis, those older than 75 years appeared to derive a similar benefit in overall survival, however. Whether triplet therapy should be universally adopted in all patients remains unclear. For example, there is a subset of patients with mHSPC with favorable- risk disease (ie, those with recurrent metastatic disease, node-only disease). In this population, the risk-benefit analysis is less clear, and whether these patients should receive this combination is not certain. Nevertheless, the results of this well-designed study are compelling and certainly represent a potential new standard treatment option for men with mHSPC. One of the strengths of this study was its large sample size that allowed for vigorous statistical analysis to evaluate the efficacy of darolutamide in combination with ADT and docetaxel.

Application for Clinical Practice

The ARASENS study provides convincing evidence that in men with mHSPC, the addition of darolutamide to docetaxel and ADT improves overall survival. This combination appeared to be well tolerated, with no evidence of increased toxicity noted. Certainly, this combination represents a potential new standard treatment option in this population; however, further understanding of which subgroups of men benefit from enhanced therapy is needed to aid in proper patient selection. 

—Santosh Kagathur, MD, and Daniel Isaac, DO, MS
Michigan State University, East Lansing, MI