Perioperative hyperglycemia, defined as blood glucose levels ≥ 180 mg/dL in the immediate pre- and postoperative period, is associated with increased postoperative morbidity, including infections, preoperative interventions, and in-hospital mortality.^1-3 Despite being identified as a barrier to optimal perioperative glycemic control, limited evidence is available on patient or health care practitioner (HCP) adherence to preoperative insulin protocols.^4-6

Background

Despite mounting evidence of the advantages of maintaining perioperative glucose levels between 80 and 180 mg/dL, available guidelines vary in their recommendations for long-acting basal insulin dosing.^7-10 The Society of Ambulatory Anesthesia suggests using 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery in patients without a history of nocturnal or morning hypoglycemia (category 2A evidence).⁹ However, the revised 2016 United Kingdom National Health Service consensus guideline recommends using 80% to 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery.⁷ The 2022 American Diabetes Association references an observational study of patients with type 2 DM (T2DM) treated with evening-only, long-acting glargine insulin, indicating that the optimal basal insulin dose on the evening before surgery is about 75% of the outpatient dose.^5,10 However, in a randomized, prospective open trial of patients with DM treated with evening-only long-acting basal insulin, no significant difference was noted in the target day of surgery (DOS) glucose levels among different dosing strategies on the evening before surgery.⁶ Presently, the optimal dose of long-acting insulin analogs on the evening before surgery is unknown.

Additionally, little is known about the other factors that influence perioperative glycemic control. Several barriers to optimal perioperative care of patients with DM have been identified, including lack of prioritization by HCPs, lack of knowledge about current evidence-based recommendations, and lack of patient information and involvement.⁴ To determine the effect of patient adherence to preoperative medication instructions on postoperative outcome, a cross-sectional study assessed surgical patients admitted to the postanesthetic care unit (PACU) and found that only 70% of patients with insulin-treated DM took their medications preoperatively. Additionally, 23% of nonadherent patients who omitted their medications either did not understand or forgot preoperative medication management instructions. Preoperative DM medication omission was associated with higher rates of hyperglycemia in the PACU (23.8% vs 3.6%; P = .02).¹¹ Importantly, to our knowledge, the extent of HCP adherence to DM management protocols and the subsequent effect on DOS hyperglycemia has not been examined until now.For patients with DM treated with an evening dose of long-acting basal insulin (ie, either once-daily long-acting basal insulin in the evening or twice-daily long-acting basal insulin, both morning and evening) presenting for elective noncardiac surgery, our aim was to decrease the rate of DOS hyperglycemia from 29% (our baseline) to 15% by intensifying the dose of insulin on the evening before surgery without increasing the rate of hypoglycemia. We also sought to determine the rates of HCP adherence to our insulin protocols as well as patients’ self-reported adherence to HCP instructions over the course of this quality improvement (QI) initiative.

Quality Improvement Program

Our surgical department consists of 11 surgical subspecialties that performed approximately 4400 noncardiac surgeries in 2019. All patients undergoing elective surgery are evaluated in the preoperative clinic, which is staffed by an anesthesiology professional (attending and resident physicians, nurse practitioners, and physician assistants) and internal medicine attending physicians. At the preoperative visit, each patient is evaluated by anesthesiology; medically complex patients may also be referred to an internal medicine professional for further risk stratification and optimization before surgery.

At the preoperative clinic visit, HCPs prepare written patient instructions for the preoperative management of medications, including glucose-lowering medications, based on a DM management protocol that was implemented in 2016 for the preoperative management of insulin, noninsulin injectable agents, and oral hyperglycemic agents. According to this protocol, patients with DM treated with evening long-acting basal insulin (eg, glargine insulin) are instructed to take 50% of their usual evening dose the evening before surgery. A preoperative clinic nurse reviews the final preoperative medication instructions with the patient at the end of the clinic visit. Patients are also instructed to avoid oral intake other than water and necessary medications after midnight before surgery regardless of the time of surgery. On the DOS, the patient’s blood glucose level is measured on arrival to the presurgical area.

Our QI initiative focused only on the dose of self-administered, long-acting basal insulin on the evening before surgery. The effect of the morning of surgery long-acting insulin dose on the DOS glucose levels largely depends on the timing of surgery, which is variable; therefore, we did not target this dose for our initiative. Patients receiving intermediate-acting neutral protamine Hagedorn (NPH) insulin were excluded because our protocol does not recommend a dose reduction for NPH insulin on the evening before surgery.

We developed a comprehensive driver diagram to help elucidate the different factors contributing to DOS hyperglycemia and to guide specific QI interventions.¹² Some of the identified contributors to DOS hyperglycemia, such as the length of preoperative fasting and timing of surgery, are unpredictable and were deemed difficult to address preoperatively. Other contributors to DOS hyperglycemia, such as outpatient DM management, often require interventions over several months, which is well beyond the time usually allotted for preoperative evaluation and optimization. On the other hand, immediate preoperative insulin dosing directly affects DOS glycemic control; therefore, improvement of the preoperative insulin management protocol to optimize the dosage on the evening before surgery was considered to be an achievable QI goal with the potential for decreasing the rate of DOS hyperglycemia in patients presenting for elective noncardiac surgery.

We used the Model for Understanding Success in Quality (MUSIQ) as a framework to identify key contextual factors that may affect the success of our QI project.¹³ Limited resource availability and difficulty with dissemination of protocol changes in the preoperative clinic were determined to be potential barriers to the successful implementation of our QI initiative. Nonetheless, senior leadership support, microsystem QI culture, QI team skills, and physician involvement supported the implementation. The revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines were followed for this study.¹⁴

Interventions

With stakeholder input from anesthesiology, internal medicine, endocrinology, and nursing, we designed an intervention to iteratively change the HCP protocol instructions for long-acting insulin dosing on the evening before surgery. In phase 1 of the study (October 1, 2018, to March 11, 2019), we obtained baseline data on the rates of DOS hyperglycemia (blood glucose ≥ 180 mg/dL) and hypoglycemia (blood glucose < 80 mg/dL), as well as patient and HCP adherence rates to our existing preoperative DM protocol. For phase 2 (March 12, 2019, to July 22, 2019), the preoperative DM management protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with hemoglobin A_1c (HbA_1c) levels > 8% from 50% of the usual outpatient dose to 100%. Finally, in phase 3 (July 23, 2019, to March 12, 2020), the protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with HbA_1c levels ≤ 8% from 50% of the usual outpatient dose to 75% while sustaining the phase 2 change. Preoperative HCPs were informed of the protocol changes in person and were provided with electronic and hard copies of each new protocol.

Protocol

We used a prospective cohort design of 424 consecutive patients with DM who presented for preoperative evaluation for elective noncardiac surgery between October 1, 2018, and March 12, 2020. For the subset of 195 patients treated with an evening dose of long-acting basal insulin, we examined the effect of intensification of this preoperative basal insulin dose on DOS hyperglycemia and hypoglycemia, HCP adherence to iterative changes of the protocol, and patient adherence to HCP instructions on preoperative medication dosing. The QI project was concluded when elective surgeries were paused due to the COVID-19 pandemic.

We created a standardized preoperative data collection form that included information on the most recent HbA_1c, time, dose, and type of patient-administered insulin on the evening before surgery, and DOS blood glucose level. A preoperative clinic nurse completed the standardized preoperative data collection form. The HCP’s preoperative medication instructions and the preoperative data collection forms were gathered for review and data analysis.

The primary outcome was DOS hyperglycemia (blood glucose levels ≥ 180 mg/dL). We monitored the rate of DOS hypoglycemia (blood glucose levels < 80 mg/dL) as a balancing measure to ensure safety with long-acting basal insulin intensification. Although hypoglycemia is defined as a blood glucose level < 70 mg/dL, a target glucose range of 80 mg/dL to 180 mg/dL is recommended during the perioperative period.⁸ Therefore, we chose a more conservative definition of hypoglycemia (blood glucose levels < 80 mg/dL) to adhere to the recommended perioperative glucose target range.

Process measures included HCP adherence to each protocol change, which was assessed by comparing written preoperative patient instructions to the current protocol. Similarly, patient adherence to HCP-recommended long-acting basal insulin dosing was assessed by comparing written preoperative patient instructions to the patient’s self-reported time and dose of long-acting basal insulin on the evening before surgery. For any discrepancy between the HCP instructions and protocol or HCP-recommended dose and patient self-reported dose of long-acting basal insulin, a detailed chart review was performed to determine the etiology.

Statistical Analysis

We used the statistical process p-control chart to assess the effect of iterative changes to the preoperative long-acting basal insulin protocol on DOS hyperglycemia. The proportion defective (rate of DOS hyperglycemia) was plotted against time to determine whether the observed variations in the rate of DOS hyperglycemia over time were attributable to random common causes or special causes because of our intervention. The lower control limit (LCL) and upper control limit (UCL) define the limits of expected outcome measures in a stable process prior to introducing changes and were set at 3 SDs from the mean to balance the likelihood of type I (false-positive) and type II (false-negative) errors. Because of the variable interval sample sizes, we used the CRITBINOM function of Microsoft Excel to calculate the exact UCL satisfying the 3 SD limits of 0.99865.¹⁵ The Shewhart rules (outliers, runs or shifts, trends, sawtooth) were used to analyze the p-control chart to identify special cause signals resulting from our interventions.¹⁶ We used the statistical process t-control chart to record the time (days) between the few occurrences of DOS hypoglycemia because cases of hypoglycemia were rare.

Ethical Consideration

The Human Research Protection Program, Associate Chief of Staff for Research and Development, and Quality, Safety, and Values department reviewed this project in accordance with the Veterans Health Administration Program Guide 1200.21 and determined that it was a nonresearch operations activity; thus, approval by an institutional review board was not needed. The authors declare no competing interests.

Patient Outcomes

We prospectively followed 424 consecutive patients with DM undergoing elective noncardiac surgery from the time of the preoperative clinic evaluation until DOS; 195 patients were on evening

A subgroup analysis of DOS glucose levels in insulin-treated patients with preoperative HbA_1c levels > 8% did not demonstrate a change in the rate of

Discussion

The management and prevention of immediate perioperative hyperglycemia and glycemic variability have attracted attention as evidence has mounted for their association with postoperative morbidity and mortality.^1,2,17 Available guidelines for preventing DOS hyperglycemia vary in their recommendations for preoperative insulin management.^7-10 Notably, concerns about iatrogenic hypoglycemia often hinder efforts to lower rates of DOS hyperglycemia.⁴ We successfully implemented an iterative intensification protocol for preoperative long-acting basal insulin doses on the evening before surgery but did not observe a lower rate of hyperglycemia. Importantly, we also did not observe a higher rate of hypoglycemia on the DOS, as observed in a previous study.⁵

The observational study by Demma and colleagues found that patients receiving 75% of their evening, long-acting basal insulin dose were significantly more likely to achieve target blood glucose levels of 100 to 180 mg/dL than patients receiving no insulin at all (78% vs 0%; P = .001). However, no significant difference was noted when this group was compared with patients receiving 50% of their evening, long-acting basal insulin doses (78% vs 70%; P = .56). This is more clinically pertinent as it is generally accepted that the evening, long-acting insulin dose should not be entirely withheld on the evening before surgery.⁵

These findings are consistent with our observation that the rate of DOS hyperglycemia did not decrease with intensification of the evening, long-acting insulin dose from 50% to 100% of the prescribed dose in patients with HbA_1c levels > 8% (phase 2) and 50% to 75% of the prescribed dose in patients with HbA_1c levels ≤ 8% (phase 3). In the study by Demma and colleagues, few patients presented with preoperative hypoglycemia (2.7%) but all had received 100% of their evening, long-acting basal insulin dose, suggesting a significant increase in the rate of hypoglycemia compared with patients receiving lower doses of insulin (P = .01).⁵ However, long-term DM control as assessed by HbA_1c level was available for < 10% of the patients, making it difficult to evaluate the effect of overall DM control on the results.⁵ In our study, preoperative HbA_1c levels were available for 99.5% of the patients and only those with HbA_1c levels > 8% received 100% of their evening, long-acting insulin dose on the evening before surgery. Notably, we did not observe a higher rate of hypoglycemia in this patient population, indicating that preoperative insulin dose intensification is safe for this subgroup.

Although HCP adherence to perioperative DM management protocols has been identified as a predominant barrier to the delivery of optimal perioperative DM care, prior studies of various preoperative insulin protocols to reduce perioperative hyperglycemia have not reported HCP adherence to their insulin protocols or its effect on DOS hyperglycemia.^4-6 Additionally, patient adherence to HCP instructions is a key factor identified in our driver diagram that may influence DOS hyperglycemia, a hypothesis that is supported by a prior cross-sectional study showing an increased rate of hyperglycemia in the PACU with omission of preoperative DM medication.¹¹ In our study, patient adherence to preoperative medication management instructions was higher than reported previously and remained consistently high regardless of protocol changes, which may explain why patient adherence did not affect the rate of DOS hyperglycemia.

Although not part of our study protocol, our preoperative HCPs routinely prepare written patient instructions for the preoperative management of medications for all patients, which likely explains higher patient adherence to instructions in our study than seen in the previous study where written instructions were only encouraged.¹¹ However, HCP adherence to the protocol decreased after our phase 2 changes and was associated with a transient increase in DOS hyperglycemia rates. The DOS hyperglycemia rates returned to baseline levels with ongoing QI efforts and education to improve HCP adherence to protocol.

Limitations

Our QI initiative had several limitations. Nearly all patients were male veterans with T2DM, and most were older (range, 50-89 years). This limits the generalizability to women, younger patients, and people with type 1 DM. Additionally, our data collection relied on completion and collection of the preoperative form by different HCPs, allowing for sampling bias if some patients with DM undergoing elective noncardiac surgery were missed. Furthermore, although we could verify HCP adherence to the preoperative DM management protocols by reviewing their written instructions, we relied on patients’ self-reported adherence to the preoperative instructions. Finally, we did not evaluate postoperative blood glucose levels because the effect of intraoperative factors such as fluid, insulin, and glucocorticoid administration on postoperative glucose levels are variable. To the best of our knowledge, no other major systematic changes occurred in the preoperative care of patients with DM during the study period.

Conclusions

The findings of our QI initiative suggest that HCP adherence to preoperative DM management protocols may be a key contributor to DOS hyperglycemia and that ensuring HCP adherence may be as important as preoperative insulin dose adjustments. To our knowledge, this is the first study to report rates of HCP adherence to preoperative DM management protocols and its effect on DOS hyperglycemia. We will focus future QI efforts on optimizing HCP adherence to preoperative DM management protocols at our institution.

Acknowledgments

We thank our endocrinology expert, Dr. Kristina Utzschneider, for her guidance in designing this improvement project and our academic research coach, Dr. Helene Starks, for her help in editing the manuscript.

Perioperative hyperglycemia, defined as blood glucose levels ≥ 180 mg/dL in the immediate pre- and postoperative period, is associated with increased postoperative morbidity, including infections, preoperative interventions, and in-hospital mortality.^1-3 Despite being identified as a barrier to optimal perioperative glycemic control, limited evidence is available on patient or health care practitioner (HCP) adherence to preoperative insulin protocols.^4-6

Background

Despite mounting evidence of the advantages of maintaining perioperative glucose levels between 80 and 180 mg/dL, available guidelines vary in their recommendations for long-acting basal insulin dosing.^7-10 The Society of Ambulatory Anesthesia suggests using 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery in patients without a history of nocturnal or morning hypoglycemia (category 2A evidence).⁹ However, the revised 2016 United Kingdom National Health Service consensus guideline recommends using 80% to 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery.⁷ The 2022 American Diabetes Association references an observational study of patients with type 2 DM (T2DM) treated with evening-only, long-acting glargine insulin, indicating that the optimal basal insulin dose on the evening before surgery is about 75% of the outpatient dose.^5,10 However, in a randomized, prospective open trial of patients with DM treated with evening-only long-acting basal insulin, no significant difference was noted in the target day of surgery (DOS) glucose levels among different dosing strategies on the evening before surgery.⁶ Presently, the optimal dose of long-acting insulin analogs on the evening before surgery is unknown.

Additionally, little is known about the other factors that influence perioperative glycemic control. Several barriers to optimal perioperative care of patients with DM have been identified, including lack of prioritization by HCPs, lack of knowledge about current evidence-based recommendations, and lack of patient information and involvement.⁴ To determine the effect of patient adherence to preoperative medication instructions on postoperative outcome, a cross-sectional study assessed surgical patients admitted to the postanesthetic care unit (PACU) and found that only 70% of patients with insulin-treated DM took their medications preoperatively. Additionally, 23% of nonadherent patients who omitted their medications either did not understand or forgot preoperative medication management instructions. Preoperative DM medication omission was associated with higher rates of hyperglycemia in the PACU (23.8% vs 3.6%; P = .02).¹¹ Importantly, to our knowledge, the extent of HCP adherence to DM management protocols and the subsequent effect on DOS hyperglycemia has not been examined until now.For patients with DM treated with an evening dose of long-acting basal insulin (ie, either once-daily long-acting basal insulin in the evening or twice-daily long-acting basal insulin, both morning and evening) presenting for elective noncardiac surgery, our aim was to decrease the rate of DOS hyperglycemia from 29% (our baseline) to 15% by intensifying the dose of insulin on the evening before surgery without increasing the rate of hypoglycemia. We also sought to determine the rates of HCP adherence to our insulin protocols as well as patients’ self-reported adherence to HCP instructions over the course of this quality improvement (QI) initiative.

Quality Improvement Program

Our surgical department consists of 11 surgical subspecialties that performed approximately 4400 noncardiac surgeries in 2019. All patients undergoing elective surgery are evaluated in the preoperative clinic, which is staffed by an anesthesiology professional (attending and resident physicians, nurse practitioners, and physician assistants) and internal medicine attending physicians. At the preoperative visit, each patient is evaluated by anesthesiology; medically complex patients may also be referred to an internal medicine professional for further risk stratification and optimization before surgery.

At the preoperative clinic visit, HCPs prepare written patient instructions for the preoperative management of medications, including glucose-lowering medications, based on a DM management protocol that was implemented in 2016 for the preoperative management of insulin, noninsulin injectable agents, and oral hyperglycemic agents. According to this protocol, patients with DM treated with evening long-acting basal insulin (eg, glargine insulin) are instructed to take 50% of their usual evening dose the evening before surgery. A preoperative clinic nurse reviews the final preoperative medication instructions with the patient at the end of the clinic visit. Patients are also instructed to avoid oral intake other than water and necessary medications after midnight before surgery regardless of the time of surgery. On the DOS, the patient’s blood glucose level is measured on arrival to the presurgical area.

Our QI initiative focused only on the dose of self-administered, long-acting basal insulin on the evening before surgery. The effect of the morning of surgery long-acting insulin dose on the DOS glucose levels largely depends on the timing of surgery, which is variable; therefore, we did not target this dose for our initiative. Patients receiving intermediate-acting neutral protamine Hagedorn (NPH) insulin were excluded because our protocol does not recommend a dose reduction for NPH insulin on the evening before surgery.

We developed a comprehensive driver diagram to help elucidate the different factors contributing to DOS hyperglycemia and to guide specific QI interventions.¹² Some of the identified contributors to DOS hyperglycemia, such as the length of preoperative fasting and timing of surgery, are unpredictable and were deemed difficult to address preoperatively. Other contributors to DOS hyperglycemia, such as outpatient DM management, often require interventions over several months, which is well beyond the time usually allotted for preoperative evaluation and optimization. On the other hand, immediate preoperative insulin dosing directly affects DOS glycemic control; therefore, improvement of the preoperative insulin management protocol to optimize the dosage on the evening before surgery was considered to be an achievable QI goal with the potential for decreasing the rate of DOS hyperglycemia in patients presenting for elective noncardiac surgery.

We used the Model for Understanding Success in Quality (MUSIQ) as a framework to identify key contextual factors that may affect the success of our QI project.¹³ Limited resource availability and difficulty with dissemination of protocol changes in the preoperative clinic were determined to be potential barriers to the successful implementation of our QI initiative. Nonetheless, senior leadership support, microsystem QI culture, QI team skills, and physician involvement supported the implementation. The revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines were followed for this study.¹⁴

Interventions

With stakeholder input from anesthesiology, internal medicine, endocrinology, and nursing, we designed an intervention to iteratively change the HCP protocol instructions for long-acting insulin dosing on the evening before surgery. In phase 1 of the study (October 1, 2018, to March 11, 2019), we obtained baseline data on the rates of DOS hyperglycemia (blood glucose ≥ 180 mg/dL) and hypoglycemia (blood glucose < 80 mg/dL), as well as patient and HCP adherence rates to our existing preoperative DM protocol. For phase 2 (March 12, 2019, to July 22, 2019), the preoperative DM management protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with hemoglobin A_1c (HbA_1c) levels > 8% from 50% of the usual outpatient dose to 100%. Finally, in phase 3 (July 23, 2019, to March 12, 2020), the protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with HbA_1c levels ≤ 8% from 50% of the usual outpatient dose to 75% while sustaining the phase 2 change. Preoperative HCPs were informed of the protocol changes in person and were provided with electronic and hard copies of each new protocol.

Protocol

We used a prospective cohort design of 424 consecutive patients with DM who presented for preoperative evaluation for elective noncardiac surgery between October 1, 2018, and March 12, 2020. For the subset of 195 patients treated with an evening dose of long-acting basal insulin, we examined the effect of intensification of this preoperative basal insulin dose on DOS hyperglycemia and hypoglycemia, HCP adherence to iterative changes of the protocol, and patient adherence to HCP instructions on preoperative medication dosing. The QI project was concluded when elective surgeries were paused due to the COVID-19 pandemic.

We created a standardized preoperative data collection form that included information on the most recent HbA_1c, time, dose, and type of patient-administered insulin on the evening before surgery, and DOS blood glucose level. A preoperative clinic nurse completed the standardized preoperative data collection form. The HCP’s preoperative medication instructions and the preoperative data collection forms were gathered for review and data analysis.

The primary outcome was DOS hyperglycemia (blood glucose levels ≥ 180 mg/dL). We monitored the rate of DOS hypoglycemia (blood glucose levels < 80 mg/dL) as a balancing measure to ensure safety with long-acting basal insulin intensification. Although hypoglycemia is defined as a blood glucose level < 70 mg/dL, a target glucose range of 80 mg/dL to 180 mg/dL is recommended during the perioperative period.⁸ Therefore, we chose a more conservative definition of hypoglycemia (blood glucose levels < 80 mg/dL) to adhere to the recommended perioperative glucose target range.

Process measures included HCP adherence to each protocol change, which was assessed by comparing written preoperative patient instructions to the current protocol. Similarly, patient adherence to HCP-recommended long-acting basal insulin dosing was assessed by comparing written preoperative patient instructions to the patient’s self-reported time and dose of long-acting basal insulin on the evening before surgery. For any discrepancy between the HCP instructions and protocol or HCP-recommended dose and patient self-reported dose of long-acting basal insulin, a detailed chart review was performed to determine the etiology.

Statistical Analysis

We used the statistical process p-control chart to assess the effect of iterative changes to the preoperative long-acting basal insulin protocol on DOS hyperglycemia. The proportion defective (rate of DOS hyperglycemia) was plotted against time to determine whether the observed variations in the rate of DOS hyperglycemia over time were attributable to random common causes or special causes because of our intervention. The lower control limit (LCL) and upper control limit (UCL) define the limits of expected outcome measures in a stable process prior to introducing changes and were set at 3 SDs from the mean to balance the likelihood of type I (false-positive) and type II (false-negative) errors. Because of the variable interval sample sizes, we used the CRITBINOM function of Microsoft Excel to calculate the exact UCL satisfying the 3 SD limits of 0.99865.¹⁵ The Shewhart rules (outliers, runs or shifts, trends, sawtooth) were used to analyze the p-control chart to identify special cause signals resulting from our interventions.¹⁶ We used the statistical process t-control chart to record the time (days) between the few occurrences of DOS hypoglycemia because cases of hypoglycemia were rare.

Ethical Consideration

The Human Research Protection Program, Associate Chief of Staff for Research and Development, and Quality, Safety, and Values department reviewed this project in accordance with the Veterans Health Administration Program Guide 1200.21 and determined that it was a nonresearch operations activity; thus, approval by an institutional review board was not needed. The authors declare no competing interests.

Patient Outcomes

We prospectively followed 424 consecutive patients with DM undergoing elective noncardiac surgery from the time of the preoperative clinic evaluation until DOS; 195 patients were on evening

A subgroup analysis of DOS glucose levels in insulin-treated patients with preoperative HbA_1c levels > 8% did not demonstrate a change in the rate of

Discussion

The management and prevention of immediate perioperative hyperglycemia and glycemic variability have attracted attention as evidence has mounted for their association with postoperative morbidity and mortality.^1,2,17 Available guidelines for preventing DOS hyperglycemia vary in their recommendations for preoperative insulin management.^7-10 Notably, concerns about iatrogenic hypoglycemia often hinder efforts to lower rates of DOS hyperglycemia.⁴ We successfully implemented an iterative intensification protocol for preoperative long-acting basal insulin doses on the evening before surgery but did not observe a lower rate of hyperglycemia. Importantly, we also did not observe a higher rate of hypoglycemia on the DOS, as observed in a previous study.⁵

The observational study by Demma and colleagues found that patients receiving 75% of their evening, long-acting basal insulin dose were significantly more likely to achieve target blood glucose levels of 100 to 180 mg/dL than patients receiving no insulin at all (78% vs 0%; P = .001). However, no significant difference was noted when this group was compared with patients receiving 50% of their evening, long-acting basal insulin doses (78% vs 70%; P = .56). This is more clinically pertinent as it is generally accepted that the evening, long-acting insulin dose should not be entirely withheld on the evening before surgery.⁵

These findings are consistent with our observation that the rate of DOS hyperglycemia did not decrease with intensification of the evening, long-acting insulin dose from 50% to 100% of the prescribed dose in patients with HbA_1c levels > 8% (phase 2) and 50% to 75% of the prescribed dose in patients with HbA_1c levels ≤ 8% (phase 3). In the study by Demma and colleagues, few patients presented with preoperative hypoglycemia (2.7%) but all had received 100% of their evening, long-acting basal insulin dose, suggesting a significant increase in the rate of hypoglycemia compared with patients receiving lower doses of insulin (P = .01).⁵ However, long-term DM control as assessed by HbA_1c level was available for < 10% of the patients, making it difficult to evaluate the effect of overall DM control on the results.⁵ In our study, preoperative HbA_1c levels were available for 99.5% of the patients and only those with HbA_1c levels > 8% received 100% of their evening, long-acting insulin dose on the evening before surgery. Notably, we did not observe a higher rate of hypoglycemia in this patient population, indicating that preoperative insulin dose intensification is safe for this subgroup.

Although HCP adherence to perioperative DM management protocols has been identified as a predominant barrier to the delivery of optimal perioperative DM care, prior studies of various preoperative insulin protocols to reduce perioperative hyperglycemia have not reported HCP adherence to their insulin protocols or its effect on DOS hyperglycemia.^4-6 Additionally, patient adherence to HCP instructions is a key factor identified in our driver diagram that may influence DOS hyperglycemia, a hypothesis that is supported by a prior cross-sectional study showing an increased rate of hyperglycemia in the PACU with omission of preoperative DM medication.¹¹ In our study, patient adherence to preoperative medication management instructions was higher than reported previously and remained consistently high regardless of protocol changes, which may explain why patient adherence did not affect the rate of DOS hyperglycemia.

Although not part of our study protocol, our preoperative HCPs routinely prepare written patient instructions for the preoperative management of medications for all patients, which likely explains higher patient adherence to instructions in our study than seen in the previous study where written instructions were only encouraged.¹¹ However, HCP adherence to the protocol decreased after our phase 2 changes and was associated with a transient increase in DOS hyperglycemia rates. The DOS hyperglycemia rates returned to baseline levels with ongoing QI efforts and education to improve HCP adherence to protocol.

Limitations

Our QI initiative had several limitations. Nearly all patients were male veterans with T2DM, and most were older (range, 50-89 years). This limits the generalizability to women, younger patients, and people with type 1 DM. Additionally, our data collection relied on completion and collection of the preoperative form by different HCPs, allowing for sampling bias if some patients with DM undergoing elective noncardiac surgery were missed. Furthermore, although we could verify HCP adherence to the preoperative DM management protocols by reviewing their written instructions, we relied on patients’ self-reported adherence to the preoperative instructions. Finally, we did not evaluate postoperative blood glucose levels because the effect of intraoperative factors such as fluid, insulin, and glucocorticoid administration on postoperative glucose levels are variable. To the best of our knowledge, no other major systematic changes occurred in the preoperative care of patients with DM during the study period.

Conclusions

The findings of our QI initiative suggest that HCP adherence to preoperative DM management protocols may be a key contributor to DOS hyperglycemia and that ensuring HCP adherence may be as important as preoperative insulin dose adjustments. To our knowledge, this is the first study to report rates of HCP adherence to preoperative DM management protocols and its effect on DOS hyperglycemia. We will focus future QI efforts on optimizing HCP adherence to preoperative DM management protocols at our institution.

Acknowledgments

We thank our endocrinology expert, Dr. Kristina Utzschneider, for her guidance in designing this improvement project and our academic research coach, Dr. Helene Starks, for her help in editing the manuscript.

Perioperative hyperglycemia, defined as blood glucose levels ≥ 180 mg/dL in the immediate pre- and postoperative period, is associated with increased postoperative morbidity, including infections, preoperative interventions, and in-hospital mortality.^1-3 Despite being identified as a barrier to optimal perioperative glycemic control, limited evidence is available on patient or health care practitioner (HCP) adherence to preoperative insulin protocols.^4-6

Background

Despite mounting evidence of the advantages of maintaining perioperative glucose levels between 80 and 180 mg/dL, available guidelines vary in their recommendations for long-acting basal insulin dosing.^7-10 The Society of Ambulatory Anesthesia suggests using 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery in patients without a history of nocturnal or morning hypoglycemia (category 2A evidence).⁹ However, the revised 2016 United Kingdom National Health Service consensus guideline recommends using 80% to 100% of the prescribed evening dosage of long-acting basal insulin dose on the night before surgery.⁷ The 2022 American Diabetes Association references an observational study of patients with type 2 DM (T2DM) treated with evening-only, long-acting glargine insulin, indicating that the optimal basal insulin dose on the evening before surgery is about 75% of the outpatient dose.^5,10 However, in a randomized, prospective open trial of patients with DM treated with evening-only long-acting basal insulin, no significant difference was noted in the target day of surgery (DOS) glucose levels among different dosing strategies on the evening before surgery.⁶ Presently, the optimal dose of long-acting insulin analogs on the evening before surgery is unknown.

Additionally, little is known about the other factors that influence perioperative glycemic control. Several barriers to optimal perioperative care of patients with DM have been identified, including lack of prioritization by HCPs, lack of knowledge about current evidence-based recommendations, and lack of patient information and involvement.⁴ To determine the effect of patient adherence to preoperative medication instructions on postoperative outcome, a cross-sectional study assessed surgical patients admitted to the postanesthetic care unit (PACU) and found that only 70% of patients with insulin-treated DM took their medications preoperatively. Additionally, 23% of nonadherent patients who omitted their medications either did not understand or forgot preoperative medication management instructions. Preoperative DM medication omission was associated with higher rates of hyperglycemia in the PACU (23.8% vs 3.6%; P = .02).¹¹ Importantly, to our knowledge, the extent of HCP adherence to DM management protocols and the subsequent effect on DOS hyperglycemia has not been examined until now.For patients with DM treated with an evening dose of long-acting basal insulin (ie, either once-daily long-acting basal insulin in the evening or twice-daily long-acting basal insulin, both morning and evening) presenting for elective noncardiac surgery, our aim was to decrease the rate of DOS hyperglycemia from 29% (our baseline) to 15% by intensifying the dose of insulin on the evening before surgery without increasing the rate of hypoglycemia. We also sought to determine the rates of HCP adherence to our insulin protocols as well as patients’ self-reported adherence to HCP instructions over the course of this quality improvement (QI) initiative.

Quality Improvement Program

Our surgical department consists of 11 surgical subspecialties that performed approximately 4400 noncardiac surgeries in 2019. All patients undergoing elective surgery are evaluated in the preoperative clinic, which is staffed by an anesthesiology professional (attending and resident physicians, nurse practitioners, and physician assistants) and internal medicine attending physicians. At the preoperative visit, each patient is evaluated by anesthesiology; medically complex patients may also be referred to an internal medicine professional for further risk stratification and optimization before surgery.

At the preoperative clinic visit, HCPs prepare written patient instructions for the preoperative management of medications, including glucose-lowering medications, based on a DM management protocol that was implemented in 2016 for the preoperative management of insulin, noninsulin injectable agents, and oral hyperglycemic agents. According to this protocol, patients with DM treated with evening long-acting basal insulin (eg, glargine insulin) are instructed to take 50% of their usual evening dose the evening before surgery. A preoperative clinic nurse reviews the final preoperative medication instructions with the patient at the end of the clinic visit. Patients are also instructed to avoid oral intake other than water and necessary medications after midnight before surgery regardless of the time of surgery. On the DOS, the patient’s blood glucose level is measured on arrival to the presurgical area.

Our QI initiative focused only on the dose of self-administered, long-acting basal insulin on the evening before surgery. The effect of the morning of surgery long-acting insulin dose on the DOS glucose levels largely depends on the timing of surgery, which is variable; therefore, we did not target this dose for our initiative. Patients receiving intermediate-acting neutral protamine Hagedorn (NPH) insulin were excluded because our protocol does not recommend a dose reduction for NPH insulin on the evening before surgery.

We developed a comprehensive driver diagram to help elucidate the different factors contributing to DOS hyperglycemia and to guide specific QI interventions.¹² Some of the identified contributors to DOS hyperglycemia, such as the length of preoperative fasting and timing of surgery, are unpredictable and were deemed difficult to address preoperatively. Other contributors to DOS hyperglycemia, such as outpatient DM management, often require interventions over several months, which is well beyond the time usually allotted for preoperative evaluation and optimization. On the other hand, immediate preoperative insulin dosing directly affects DOS glycemic control; therefore, improvement of the preoperative insulin management protocol to optimize the dosage on the evening before surgery was considered to be an achievable QI goal with the potential for decreasing the rate of DOS hyperglycemia in patients presenting for elective noncardiac surgery.

We used the Model for Understanding Success in Quality (MUSIQ) as a framework to identify key contextual factors that may affect the success of our QI project.¹³ Limited resource availability and difficulty with dissemination of protocol changes in the preoperative clinic were determined to be potential barriers to the successful implementation of our QI initiative. Nonetheless, senior leadership support, microsystem QI culture, QI team skills, and physician involvement supported the implementation. The revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines were followed for this study.¹⁴

Interventions

With stakeholder input from anesthesiology, internal medicine, endocrinology, and nursing, we designed an intervention to iteratively change the HCP protocol instructions for long-acting insulin dosing on the evening before surgery. In phase 1 of the study (October 1, 2018, to March 11, 2019), we obtained baseline data on the rates of DOS hyperglycemia (blood glucose ≥ 180 mg/dL) and hypoglycemia (blood glucose < 80 mg/dL), as well as patient and HCP adherence rates to our existing preoperative DM protocol. For phase 2 (March 12, 2019, to July 22, 2019), the preoperative DM management protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with hemoglobin A_1c (HbA_1c) levels > 8% from 50% of the usual outpatient dose to 100%. Finally, in phase 3 (July 23, 2019, to March 12, 2020), the protocol was changed to increase the dose of long-acting basal insulin on the evening before surgery for patients with HbA_1c levels ≤ 8% from 50% of the usual outpatient dose to 75% while sustaining the phase 2 change. Preoperative HCPs were informed of the protocol changes in person and were provided with electronic and hard copies of each new protocol.

Protocol

We used a prospective cohort design of 424 consecutive patients with DM who presented for preoperative evaluation for elective noncardiac surgery between October 1, 2018, and March 12, 2020. For the subset of 195 patients treated with an evening dose of long-acting basal insulin, we examined the effect of intensification of this preoperative basal insulin dose on DOS hyperglycemia and hypoglycemia, HCP adherence to iterative changes of the protocol, and patient adherence to HCP instructions on preoperative medication dosing. The QI project was concluded when elective surgeries were paused due to the COVID-19 pandemic.

We created a standardized preoperative data collection form that included information on the most recent HbA_1c, time, dose, and type of patient-administered insulin on the evening before surgery, and DOS blood glucose level. A preoperative clinic nurse completed the standardized preoperative data collection form. The HCP’s preoperative medication instructions and the preoperative data collection forms were gathered for review and data analysis.

The primary outcome was DOS hyperglycemia (blood glucose levels ≥ 180 mg/dL). We monitored the rate of DOS hypoglycemia (blood glucose levels < 80 mg/dL) as a balancing measure to ensure safety with long-acting basal insulin intensification. Although hypoglycemia is defined as a blood glucose level < 70 mg/dL, a target glucose range of 80 mg/dL to 180 mg/dL is recommended during the perioperative period.⁸ Therefore, we chose a more conservative definition of hypoglycemia (blood glucose levels < 80 mg/dL) to adhere to the recommended perioperative glucose target range.

Process measures included HCP adherence to each protocol change, which was assessed by comparing written preoperative patient instructions to the current protocol. Similarly, patient adherence to HCP-recommended long-acting basal insulin dosing was assessed by comparing written preoperative patient instructions to the patient’s self-reported time and dose of long-acting basal insulin on the evening before surgery. For any discrepancy between the HCP instructions and protocol or HCP-recommended dose and patient self-reported dose of long-acting basal insulin, a detailed chart review was performed to determine the etiology.

Statistical Analysis

We used the statistical process p-control chart to assess the effect of iterative changes to the preoperative long-acting basal insulin protocol on DOS hyperglycemia. The proportion defective (rate of DOS hyperglycemia) was plotted against time to determine whether the observed variations in the rate of DOS hyperglycemia over time were attributable to random common causes or special causes because of our intervention. The lower control limit (LCL) and upper control limit (UCL) define the limits of expected outcome measures in a stable process prior to introducing changes and were set at 3 SDs from the mean to balance the likelihood of type I (false-positive) and type II (false-negative) errors. Because of the variable interval sample sizes, we used the CRITBINOM function of Microsoft Excel to calculate the exact UCL satisfying the 3 SD limits of 0.99865.¹⁵ The Shewhart rules (outliers, runs or shifts, trends, sawtooth) were used to analyze the p-control chart to identify special cause signals resulting from our interventions.¹⁶ We used the statistical process t-control chart to record the time (days) between the few occurrences of DOS hypoglycemia because cases of hypoglycemia were rare.

Ethical Consideration

The Human Research Protection Program, Associate Chief of Staff for Research and Development, and Quality, Safety, and Values department reviewed this project in accordance with the Veterans Health Administration Program Guide 1200.21 and determined that it was a nonresearch operations activity; thus, approval by an institutional review board was not needed. The authors declare no competing interests.

Patient Outcomes

We prospectively followed 424 consecutive patients with DM undergoing elective noncardiac surgery from the time of the preoperative clinic evaluation until DOS; 195 patients were on evening

A subgroup analysis of DOS glucose levels in insulin-treated patients with preoperative HbA_1c levels > 8% did not demonstrate a change in the rate of

Discussion

The management and prevention of immediate perioperative hyperglycemia and glycemic variability have attracted attention as evidence has mounted for their association with postoperative morbidity and mortality.^1,2,17 Available guidelines for preventing DOS hyperglycemia vary in their recommendations for preoperative insulin management.^7-10 Notably, concerns about iatrogenic hypoglycemia often hinder efforts to lower rates of DOS hyperglycemia.⁴ We successfully implemented an iterative intensification protocol for preoperative long-acting basal insulin doses on the evening before surgery but did not observe a lower rate of hyperglycemia. Importantly, we also did not observe a higher rate of hypoglycemia on the DOS, as observed in a previous study.⁵

The observational study by Demma and colleagues found that patients receiving 75% of their evening, long-acting basal insulin dose were significantly more likely to achieve target blood glucose levels of 100 to 180 mg/dL than patients receiving no insulin at all (78% vs 0%; P = .001). However, no significant difference was noted when this group was compared with patients receiving 50% of their evening, long-acting basal insulin doses (78% vs 70%; P = .56). This is more clinically pertinent as it is generally accepted that the evening, long-acting insulin dose should not be entirely withheld on the evening before surgery.⁵

These findings are consistent with our observation that the rate of DOS hyperglycemia did not decrease with intensification of the evening, long-acting insulin dose from 50% to 100% of the prescribed dose in patients with HbA_1c levels > 8% (phase 2) and 50% to 75% of the prescribed dose in patients with HbA_1c levels ≤ 8% (phase 3). In the study by Demma and colleagues, few patients presented with preoperative hypoglycemia (2.7%) but all had received 100% of their evening, long-acting basal insulin dose, suggesting a significant increase in the rate of hypoglycemia compared with patients receiving lower doses of insulin (P = .01).⁵ However, long-term DM control as assessed by HbA_1c level was available for < 10% of the patients, making it difficult to evaluate the effect of overall DM control on the results.⁵ In our study, preoperative HbA_1c levels were available for 99.5% of the patients and only those with HbA_1c levels > 8% received 100% of their evening, long-acting insulin dose on the evening before surgery. Notably, we did not observe a higher rate of hypoglycemia in this patient population, indicating that preoperative insulin dose intensification is safe for this subgroup.

Although HCP adherence to perioperative DM management protocols has been identified as a predominant barrier to the delivery of optimal perioperative DM care, prior studies of various preoperative insulin protocols to reduce perioperative hyperglycemia have not reported HCP adherence to their insulin protocols or its effect on DOS hyperglycemia.^4-6 Additionally, patient adherence to HCP instructions is a key factor identified in our driver diagram that may influence DOS hyperglycemia, a hypothesis that is supported by a prior cross-sectional study showing an increased rate of hyperglycemia in the PACU with omission of preoperative DM medication.¹¹ In our study, patient adherence to preoperative medication management instructions was higher than reported previously and remained consistently high regardless of protocol changes, which may explain why patient adherence did not affect the rate of DOS hyperglycemia.

Although not part of our study protocol, our preoperative HCPs routinely prepare written patient instructions for the preoperative management of medications for all patients, which likely explains higher patient adherence to instructions in our study than seen in the previous study where written instructions were only encouraged.¹¹ However, HCP adherence to the protocol decreased after our phase 2 changes and was associated with a transient increase in DOS hyperglycemia rates. The DOS hyperglycemia rates returned to baseline levels with ongoing QI efforts and education to improve HCP adherence to protocol.

Limitations

Our QI initiative had several limitations. Nearly all patients were male veterans with T2DM, and most were older (range, 50-89 years). This limits the generalizability to women, younger patients, and people with type 1 DM. Additionally, our data collection relied on completion and collection of the preoperative form by different HCPs, allowing for sampling bias if some patients with DM undergoing elective noncardiac surgery were missed. Furthermore, although we could verify HCP adherence to the preoperative DM management protocols by reviewing their written instructions, we relied on patients’ self-reported adherence to the preoperative instructions. Finally, we did not evaluate postoperative blood glucose levels because the effect of intraoperative factors such as fluid, insulin, and glucocorticoid administration on postoperative glucose levels are variable. To the best of our knowledge, no other major systematic changes occurred in the preoperative care of patients with DM during the study period.

Conclusions

The findings of our QI initiative suggest that HCP adherence to preoperative DM management protocols may be a key contributor to DOS hyperglycemia and that ensuring HCP adherence may be as important as preoperative insulin dose adjustments. To our knowledge, this is the first study to report rates of HCP adherence to preoperative DM management protocols and its effect on DOS hyperglycemia. We will focus future QI efforts on optimizing HCP adherence to preoperative DM management protocols at our institution.

Acknowledgments

We thank our endocrinology expert, Dr. Kristina Utzschneider, for her guidance in designing this improvement project and our academic research coach, Dr. Helene Starks, for her help in editing the manuscript.

Prolonged intensive care unit (ICU) stays, variably defined as > 48 h to > 14 days, are a known complication of cardiac surgery.^1-8 Prolonged stays are associated with higher resource utilization and higher mortality.^2,3,9-12 Although there are several cardiac surgery risk models that can be used preoperatively to identify patients at risk for prolonged ICU stay, factors that influence outcomes for patients who experience prolonged ICU stays are poorly understood.^2,13-19 Little information is available to inform discussions between health care practitioners (HCPs) and patients throughout a prolonged ICU stay, especially those ≥ 7 days.

As cardiac surgical complexity, patient age, and preexisting comorbidities have increased over time, so has the need to provide patients and HCPs with data to inform decision making, enhance prognostication, and set realistic expectations at varying time intervals during prolonged ICU stay. The purpose of this study was to evaluate short- and long-term outcomes in cardiac surgery patients after prolonged ICU stays at relevant time intervals (7, 14, 21, and 28 days) and to determine factors that may predict a patient’s outcome after a prolonged ICU stay.

Methods

The University of Michigan Health System Institutional Review Board approved this study and waived informed consent. We merged the University of Michigan Medical Center Society of Thoracic Surgeons (STS) database, which is updated periodically with late mortality, with elements of the electronic health record (EHR). Adult patients were included if they had cardiac surgery at the University of Michigan between January 2, 2001, and December 31, 2011. Late mortality was updated through December 1, 2014. Data are presented as frequency (%), mean (SD), and median (IQR) as appropriate. Bivariate comparisons between survivors and nonsurvivors were done with χ² or Fisher exact test for categorical data, Student t test for continuous normally distributed data, and Wilcoxon rank sum test for continuous not normally distributed data. To determine factors associated with operative mortality (death within 30 days of surgery or hospital discharge, whichever occurred later), we used logistic regression with forward selection. All available factors were initially entered in the models.

Separate logistic models were created based on all data available at days 7, 14, 21, and 28. Final models consisted of factors with statistically significant P values (< .05) and adjusted odds ratios (AORs) with 95% CIs that excluded 1. To determine factors associated with late mortality, we used a Cox proportional hazard model, which used data available at discharge and STS complications. As these complications did not include their timing, they could only be used in models created at discharge and not for days 7, 14, 21, and 28 models. Final models consisted of factors with P values < .05 and 95% CIs of the AORs or the hazard ratios (HRs) that excluded 1. As the EHR did not start recording data until January 2, 2004, and its capture of data remained incomplete for several years, rather than imputing these missing data or excluding these patients, we chose to create an extra categorical level for each factor to represent missing data. For continuous factors with missing data, we first converted the continuous data to terciles and the missing data became the fourth level.^20,21

The discrimination of the logistic models were determined by the c-statistic and for the Cox proportional hazards model with the Harrell concordance index (C index). Time trends were assessed with the Cochran-Armitage trend test. P < .05 was deemed statistically significant. Statistics were calculated with SPSS versions 21-23 or SAS 9.4.

Results

Of 8309 admissions to the ICU after cardiac surgery, 1174 (14%) had ICU stays ≥ 7 days, 386 (5%) ≥ 14 days, 201 (2%) ≥ 21 days, and 80 (0.9%) ≥ 28 days. The prolonged ICU study population was mostly male, White race, with a mean (SD) age of 62 (14) years. Patients had a variety of comorbidities, most notably 61% had hypertension and half had heart failure. Valve surgery (55%) was the most common procedure (n = 651). Twenty-nine percent required > 1 procedure (eAppendix 1).

The operative mortality

Using multivariable logistic regression to adjust for factors associated with mortality, we found that receiving mechanical ventilation on the day of analysis was associated with increased operative mortality with AOR increasing from 3.35 (95% CI, 2.82-3.98) for

Discussion

We found that operative mortality increased the longer the patient stayed in the ICU, ranging from 11% for ≥ 7 days to 35% for ≥ 28 days. We further found that in ICU survivors, median (IQR) survival was 10.7 (0.7) years. While previous studies have evaluated prolonged ICU stays, they have been limited by studying limited subpopulations, such as patients who are dependent on dialysis or octogenarians, or used a single cutoff to define prolonged ICU stays, variably defined from > 48 hours to > 14 days.^2-7,9-12,22 Our study is similar to others that used ≥ 2 cutoffs.^1,8 However, our study was novel by providing 4 cutoffs to improve temporal prediction of hospital outcomes. Unlike a study by Ryan and colleagues, which found no increase in mortality with longer stay (43.5% for ≥ 14 days and 45% for ≥ 28 days), our study findings are similar to those of Yu and colleagues (11.1% mortality for prolonged ICU stays of 1 to 2 weeks, 26.6% for 2 to 4 weeks, and 31% for > 4 weeks) and others (8%, 3 to 14 days; 40%, >14 days; 10%, 1 to 2 weeks; 25.7% > 2 weeks) in finding a progressively increased hospital mortality with longer ICU stays.^1,4,5,8 These differences may be related to different ICU populations or to improvements in care since Ryan and colleagues study was conducted.

Fewer studies have evaluated factors associated with mortality in cardiac surgery prolonged ICU stay patients. Our study is similar to other studies that evaluated risk factors by finding associations between a variety of comorbidities and process of care associated with both operative and long-term mortality; however, comparison between these studies is limited by the varying factors analyzed.^{1,3,5,6,8,9,11} We found that mechanical ventilation on days 7, 14, 21, and 28 was strongly associated with operative mortality, similar to noncardiac surgery patients and cardiac surgery patients.^6,23,24 While we found several processes of care, such as catecholamine use and transfusions to be associated with mortality, which is similar to other studies, notably, we did not find an association between renal replacement therapy and mortality.^1,25 While there is an association between renal replacement therapy and mortality in ICU patients, its status in cardiac surgery patients with prolonged ICU stays is less clear.²⁶ While Ryan and colleagues found an association between renal replacement therapy and hospital mortality in patients staying ≥ 14 days, they did not find it in patients staying ≥.

28 days.¹ Other studies of prolonged ICU stays for cardiac surgery patients have also failed to find an association between renal replacement therapy and mortality.^5,6,9 Importantly, practice that expedites liberation from mechanical ventilation, such as fast tracking, daily spontaneous breathing trials, extubation to noninvasive respiratory support, and pulmonary rehabilitation may all have potential to limit mechanical ventilation duration and improve hospital survival and deserve further study.^27-29Median (IQR) survival in hospital survivors was 10.7 (0.7) years, which is generally better than previously reported, but similar to that reported by Silberman and colleagues.^{2,4,6,8,11,12} Differences between these studies may relate to different patient populations within the cardiac surgery ICUs, definitions of prolonged ICU stays, or eras of care. Further study is needed to clarify these discrepancies. We found that cardiac transplantation and obesity were associated with the least risk of dying, while smoking, lung disease, and postoperative pneumonia were independently associated with increased hazard of dying. The obesity paradox, where obesity is protective, has been previously observed in cardiac surgery patients.³⁰

Strengths and Limitations

There are several limitations of this study. This is a single center study, and our patient population and processes of care may differ from other centers, limiting its generalizability. Notably, we do fewer coronary bypass operations and more aortic reconstructions and ventricular assist device insertions than do many other centers. Second, we did not have laboratory values for about one-third of patients (preceded EHR implementation). However, we were able to compensate for this by binning values and including missing data as an extra bin.^20,21

The main strength of this study is that we were able to combine disparate records to assess a large number of potential factors associated with both operative and long-term mortality. This produced models that had good to very good discrimination. By producing models at 7, 14, 21, and 28 days to predict operative mortality and a model at discharge, it may help to provide objective data to facilitate conversations with patients and their families. However, further studies to externally validate these models should be conducted.

Conclusions

We found that longer prolonged ICU stays are associated with both operative and late mortality. Receiving mechanical ventilation on days 7, 14, 21, or 28 was strongly associated with operative mortality.

Prolonged intensive care unit (ICU) stays, variably defined as > 48 h to > 14 days, are a known complication of cardiac surgery.^1-8 Prolonged stays are associated with higher resource utilization and higher mortality.^2,3,9-12 Although there are several cardiac surgery risk models that can be used preoperatively to identify patients at risk for prolonged ICU stay, factors that influence outcomes for patients who experience prolonged ICU stays are poorly understood.^2,13-19 Little information is available to inform discussions between health care practitioners (HCPs) and patients throughout a prolonged ICU stay, especially those ≥ 7 days.

As cardiac surgical complexity, patient age, and preexisting comorbidities have increased over time, so has the need to provide patients and HCPs with data to inform decision making, enhance prognostication, and set realistic expectations at varying time intervals during prolonged ICU stay. The purpose of this study was to evaluate short- and long-term outcomes in cardiac surgery patients after prolonged ICU stays at relevant time intervals (7, 14, 21, and 28 days) and to determine factors that may predict a patient’s outcome after a prolonged ICU stay.

Methods

The University of Michigan Health System Institutional Review Board approved this study and waived informed consent. We merged the University of Michigan Medical Center Society of Thoracic Surgeons (STS) database, which is updated periodically with late mortality, with elements of the electronic health record (EHR). Adult patients were included if they had cardiac surgery at the University of Michigan between January 2, 2001, and December 31, 2011. Late mortality was updated through December 1, 2014. Data are presented as frequency (%), mean (SD), and median (IQR) as appropriate. Bivariate comparisons between survivors and nonsurvivors were done with χ² or Fisher exact test for categorical data, Student t test for continuous normally distributed data, and Wilcoxon rank sum test for continuous not normally distributed data. To determine factors associated with operative mortality (death within 30 days of surgery or hospital discharge, whichever occurred later), we used logistic regression with forward selection. All available factors were initially entered in the models.

Separate logistic models were created based on all data available at days 7, 14, 21, and 28. Final models consisted of factors with statistically significant P values (< .05) and adjusted odds ratios (AORs) with 95% CIs that excluded 1. To determine factors associated with late mortality, we used a Cox proportional hazard model, which used data available at discharge and STS complications. As these complications did not include their timing, they could only be used in models created at discharge and not for days 7, 14, 21, and 28 models. Final models consisted of factors with P values < .05 and 95% CIs of the AORs or the hazard ratios (HRs) that excluded 1. As the EHR did not start recording data until January 2, 2004, and its capture of data remained incomplete for several years, rather than imputing these missing data or excluding these patients, we chose to create an extra categorical level for each factor to represent missing data. For continuous factors with missing data, we first converted the continuous data to terciles and the missing data became the fourth level.^20,21

The discrimination of the logistic models were determined by the c-statistic and for the Cox proportional hazards model with the Harrell concordance index (C index). Time trends were assessed with the Cochran-Armitage trend test. P < .05 was deemed statistically significant. Statistics were calculated with SPSS versions 21-23 or SAS 9.4.

Results

Of 8309 admissions to the ICU after cardiac surgery, 1174 (14%) had ICU stays ≥ 7 days, 386 (5%) ≥ 14 days, 201 (2%) ≥ 21 days, and 80 (0.9%) ≥ 28 days. The prolonged ICU study population was mostly male, White race, with a mean (SD) age of 62 (14) years. Patients had a variety of comorbidities, most notably 61% had hypertension and half had heart failure. Valve surgery (55%) was the most common procedure (n = 651). Twenty-nine percent required > 1 procedure (eAppendix 1).

The operative mortality

Using multivariable logistic regression to adjust for factors associated with mortality, we found that receiving mechanical ventilation on the day of analysis was associated with increased operative mortality with AOR increasing from 3.35 (95% CI, 2.82-3.98) for

Discussion

We found that operative mortality increased the longer the patient stayed in the ICU, ranging from 11% for ≥ 7 days to 35% for ≥ 28 days. We further found that in ICU survivors, median (IQR) survival was 10.7 (0.7) years. While previous studies have evaluated prolonged ICU stays, they have been limited by studying limited subpopulations, such as patients who are dependent on dialysis or octogenarians, or used a single cutoff to define prolonged ICU stays, variably defined from > 48 hours to > 14 days.^2-7,9-12,22 Our study is similar to others that used ≥ 2 cutoffs.^1,8 However, our study was novel by providing 4 cutoffs to improve temporal prediction of hospital outcomes. Unlike a study by Ryan and colleagues, which found no increase in mortality with longer stay (43.5% for ≥ 14 days and 45% for ≥ 28 days), our study findings are similar to those of Yu and colleagues (11.1% mortality for prolonged ICU stays of 1 to 2 weeks, 26.6% for 2 to 4 weeks, and 31% for > 4 weeks) and others (8%, 3 to 14 days; 40%, >14 days; 10%, 1 to 2 weeks; 25.7% > 2 weeks) in finding a progressively increased hospital mortality with longer ICU stays.^1,4,5,8 These differences may be related to different ICU populations or to improvements in care since Ryan and colleagues study was conducted.

Fewer studies have evaluated factors associated with mortality in cardiac surgery prolonged ICU stay patients. Our study is similar to other studies that evaluated risk factors by finding associations between a variety of comorbidities and process of care associated with both operative and long-term mortality; however, comparison between these studies is limited by the varying factors analyzed.^{1,3,5,6,8,9,11} We found that mechanical ventilation on days 7, 14, 21, and 28 was strongly associated with operative mortality, similar to noncardiac surgery patients and cardiac surgery patients.^6,23,24 While we found several processes of care, such as catecholamine use and transfusions to be associated with mortality, which is similar to other studies, notably, we did not find an association between renal replacement therapy and mortality.^1,25 While there is an association between renal replacement therapy and mortality in ICU patients, its status in cardiac surgery patients with prolonged ICU stays is less clear.²⁶ While Ryan and colleagues found an association between renal replacement therapy and hospital mortality in patients staying ≥ 14 days, they did not find it in patients staying ≥.

28 days.¹ Other studies of prolonged ICU stays for cardiac surgery patients have also failed to find an association between renal replacement therapy and mortality.^5,6,9 Importantly, practice that expedites liberation from mechanical ventilation, such as fast tracking, daily spontaneous breathing trials, extubation to noninvasive respiratory support, and pulmonary rehabilitation may all have potential to limit mechanical ventilation duration and improve hospital survival and deserve further study.^27-29Median (IQR) survival in hospital survivors was 10.7 (0.7) years, which is generally better than previously reported, but similar to that reported by Silberman and colleagues.^{2,4,6,8,11,12} Differences between these studies may relate to different patient populations within the cardiac surgery ICUs, definitions of prolonged ICU stays, or eras of care. Further study is needed to clarify these discrepancies. We found that cardiac transplantation and obesity were associated with the least risk of dying, while smoking, lung disease, and postoperative pneumonia were independently associated with increased hazard of dying. The obesity paradox, where obesity is protective, has been previously observed in cardiac surgery patients.³⁰

Strengths and Limitations

There are several limitations of this study. This is a single center study, and our patient population and processes of care may differ from other centers, limiting its generalizability. Notably, we do fewer coronary bypass operations and more aortic reconstructions and ventricular assist device insertions than do many other centers. Second, we did not have laboratory values for about one-third of patients (preceded EHR implementation). However, we were able to compensate for this by binning values and including missing data as an extra bin.^20,21

The main strength of this study is that we were able to combine disparate records to assess a large number of potential factors associated with both operative and long-term mortality. This produced models that had good to very good discrimination. By producing models at 7, 14, 21, and 28 days to predict operative mortality and a model at discharge, it may help to provide objective data to facilitate conversations with patients and their families. However, further studies to externally validate these models should be conducted.

Conclusions

We found that longer prolonged ICU stays are associated with both operative and late mortality. Receiving mechanical ventilation on days 7, 14, 21, or 28 was strongly associated with operative mortality.

Prolonged intensive care unit (ICU) stays, variably defined as > 48 h to > 14 days, are a known complication of cardiac surgery.^1-8 Prolonged stays are associated with higher resource utilization and higher mortality.^2,3,9-12 Although there are several cardiac surgery risk models that can be used preoperatively to identify patients at risk for prolonged ICU stay, factors that influence outcomes for patients who experience prolonged ICU stays are poorly understood.^2,13-19 Little information is available to inform discussions between health care practitioners (HCPs) and patients throughout a prolonged ICU stay, especially those ≥ 7 days.

As cardiac surgical complexity, patient age, and preexisting comorbidities have increased over time, so has the need to provide patients and HCPs with data to inform decision making, enhance prognostication, and set realistic expectations at varying time intervals during prolonged ICU stay. The purpose of this study was to evaluate short- and long-term outcomes in cardiac surgery patients after prolonged ICU stays at relevant time intervals (7, 14, 21, and 28 days) and to determine factors that may predict a patient’s outcome after a prolonged ICU stay.

Methods

The University of Michigan Health System Institutional Review Board approved this study and waived informed consent. We merged the University of Michigan Medical Center Society of Thoracic Surgeons (STS) database, which is updated periodically with late mortality, with elements of the electronic health record (EHR). Adult patients were included if they had cardiac surgery at the University of Michigan between January 2, 2001, and December 31, 2011. Late mortality was updated through December 1, 2014. Data are presented as frequency (%), mean (SD), and median (IQR) as appropriate. Bivariate comparisons between survivors and nonsurvivors were done with χ² or Fisher exact test for categorical data, Student t test for continuous normally distributed data, and Wilcoxon rank sum test for continuous not normally distributed data. To determine factors associated with operative mortality (death within 30 days of surgery or hospital discharge, whichever occurred later), we used logistic regression with forward selection. All available factors were initially entered in the models.

Separate logistic models were created based on all data available at days 7, 14, 21, and 28. Final models consisted of factors with statistically significant P values (< .05) and adjusted odds ratios (AORs) with 95% CIs that excluded 1. To determine factors associated with late mortality, we used a Cox proportional hazard model, which used data available at discharge and STS complications. As these complications did not include their timing, they could only be used in models created at discharge and not for days 7, 14, 21, and 28 models. Final models consisted of factors with P values < .05 and 95% CIs of the AORs or the hazard ratios (HRs) that excluded 1. As the EHR did not start recording data until January 2, 2004, and its capture of data remained incomplete for several years, rather than imputing these missing data or excluding these patients, we chose to create an extra categorical level for each factor to represent missing data. For continuous factors with missing data, we first converted the continuous data to terciles and the missing data became the fourth level.^20,21

The discrimination of the logistic models were determined by the c-statistic and for the Cox proportional hazards model with the Harrell concordance index (C index). Time trends were assessed with the Cochran-Armitage trend test. P < .05 was deemed statistically significant. Statistics were calculated with SPSS versions 21-23 or SAS 9.4.

Results

Of 8309 admissions to the ICU after cardiac surgery, 1174 (14%) had ICU stays ≥ 7 days, 386 (5%) ≥ 14 days, 201 (2%) ≥ 21 days, and 80 (0.9%) ≥ 28 days. The prolonged ICU study population was mostly male, White race, with a mean (SD) age of 62 (14) years. Patients had a variety of comorbidities, most notably 61% had hypertension and half had heart failure. Valve surgery (55%) was the most common procedure (n = 651). Twenty-nine percent required > 1 procedure (eAppendix 1).

The operative mortality

Using multivariable logistic regression to adjust for factors associated with mortality, we found that receiving mechanical ventilation on the day of analysis was associated with increased operative mortality with AOR increasing from 3.35 (95% CI, 2.82-3.98) for

Discussion

We found that operative mortality increased the longer the patient stayed in the ICU, ranging from 11% for ≥ 7 days to 35% for ≥ 28 days. We further found that in ICU survivors, median (IQR) survival was 10.7 (0.7) years. While previous studies have evaluated prolonged ICU stays, they have been limited by studying limited subpopulations, such as patients who are dependent on dialysis or octogenarians, or used a single cutoff to define prolonged ICU stays, variably defined from > 48 hours to > 14 days.^2-7,9-12,22 Our study is similar to others that used ≥ 2 cutoffs.^1,8 However, our study was novel by providing 4 cutoffs to improve temporal prediction of hospital outcomes. Unlike a study by Ryan and colleagues, which found no increase in mortality with longer stay (43.5% for ≥ 14 days and 45% for ≥ 28 days), our study findings are similar to those of Yu and colleagues (11.1% mortality for prolonged ICU stays of 1 to 2 weeks, 26.6% for 2 to 4 weeks, and 31% for > 4 weeks) and others (8%, 3 to 14 days; 40%, >14 days; 10%, 1 to 2 weeks; 25.7% > 2 weeks) in finding a progressively increased hospital mortality with longer ICU stays.^1,4,5,8 These differences may be related to different ICU populations or to improvements in care since Ryan and colleagues study was conducted.

Fewer studies have evaluated factors associated with mortality in cardiac surgery prolonged ICU stay patients. Our study is similar to other studies that evaluated risk factors by finding associations between a variety of comorbidities and process of care associated with both operative and long-term mortality; however, comparison between these studies is limited by the varying factors analyzed.^{1,3,5,6,8,9,11} We found that mechanical ventilation on days 7, 14, 21, and 28 was strongly associated with operative mortality, similar to noncardiac surgery patients and cardiac surgery patients.^6,23,24 While we found several processes of care, such as catecholamine use and transfusions to be associated with mortality, which is similar to other studies, notably, we did not find an association between renal replacement therapy and mortality.^1,25 While there is an association between renal replacement therapy and mortality in ICU patients, its status in cardiac surgery patients with prolonged ICU stays is less clear.²⁶ While Ryan and colleagues found an association between renal replacement therapy and hospital mortality in patients staying ≥ 14 days, they did not find it in patients staying ≥.

28 days.¹ Other studies of prolonged ICU stays for cardiac surgery patients have also failed to find an association between renal replacement therapy and mortality.^5,6,9 Importantly, practice that expedites liberation from mechanical ventilation, such as fast tracking, daily spontaneous breathing trials, extubation to noninvasive respiratory support, and pulmonary rehabilitation may all have potential to limit mechanical ventilation duration and improve hospital survival and deserve further study.^27-29Median (IQR) survival in hospital survivors was 10.7 (0.7) years, which is generally better than previously reported, but similar to that reported by Silberman and colleagues.^{2,4,6,8,11,12} Differences between these studies may relate to different patient populations within the cardiac surgery ICUs, definitions of prolonged ICU stays, or eras of care. Further study is needed to clarify these discrepancies. We found that cardiac transplantation and obesity were associated with the least risk of dying, while smoking, lung disease, and postoperative pneumonia were independently associated with increased hazard of dying. The obesity paradox, where obesity is protective, has been previously observed in cardiac surgery patients.³⁰

Strengths and Limitations

There are several limitations of this study. This is a single center study, and our patient population and processes of care may differ from other centers, limiting its generalizability. Notably, we do fewer coronary bypass operations and more aortic reconstructions and ventricular assist device insertions than do many other centers. Second, we did not have laboratory values for about one-third of patients (preceded EHR implementation). However, we were able to compensate for this by binning values and including missing data as an extra bin.^20,21

The main strength of this study is that we were able to combine disparate records to assess a large number of potential factors associated with both operative and long-term mortality. This produced models that had good to very good discrimination. By producing models at 7, 14, 21, and 28 days to predict operative mortality and a model at discharge, it may help to provide objective data to facilitate conversations with patients and their families. However, further studies to externally validate these models should be conducted.

Conclusions

We found that longer prolonged ICU stays are associated with both operative and late mortality. Receiving mechanical ventilation on days 7, 14, 21, or 28 was strongly associated with operative mortality.

PHILADELPHIA – Women with spondyloarthritis (SpA) who are desiring pregnancy may want to consider decreasing use or discontinuing use (with supervision) of nonsteroidal anti-inflammatory drugs before conception, new data suggest.

Researchers have found a connection between NSAID use and age and a significantly longer time to conception among women with spondyloarthritis. Sabrina Hamroun, MMed, with the rheumatology department at the University Hospital Cochin, Paris, presented the findings during a press conference at the annual meeting of the American College of Rheumatology.

Subfertility group took an average of 16 months to get pregnant

Subfertility was observed in 40 (45.4%) of the women, with an average time to conception of 16.1 months. A woman was considered subfertile if her time to conception was more than 12 months or if she did not become pregnant.

The average preconception Bath Ankylosing Spondylitis Disease Activity Index score was 2.9 (+/- 2.1), the authors noted. The average age of the participants was 32 years.

Twenty-three patients were treated with NSAIDs, eight with corticosteroids, 12 with conventional synthetic disease-modifying antirheumatic drugs, and 61 with biologics.

Researchers adjusted for factors including age, body mass index, disease duration and severity, smoking, form of SpA (axial, peripheral, or both), and medication in the preconception period.

They found significant associations between longer time to conception and age (hazard ratio, 1.22; 95% confidence interval, 1.08-1.40; P < .001), and a much higher hazard ratio with the use of NSAIDs during preconception (HR, 3.01; 95% CI, 2.15-3.85; P = .01).

Some data unavailable

Ms. Hamroun acknowledged that no data were available on the frequency of sexual intercourse or quality of life, factors that could affect time to conception. Women were asked when they discontinued contraceptive use and actively began trying to become pregnant.

She stated that information on the dose of NSAIDs used by the patients was incomplete, noting, “We were therefore unable to adjust the results of our statistical analyses on the dose used by patients.”

Additionally, because the study participants were patients at tertiary centers in France and had more severe disease, the results may not be generalizable to all women of childbearing age. Patients with less severe SpA are often managed in outpatient settings in France, she said.

When asked about alternatives to NSAIDs, Ms. Hamroun said that anti–tumor necrosis factor agents with low placental passage may be a good alternative “if a woman with long-standing difficulties to conceive needs a regular use of NSAIDs to control disease activity, in the absence of any other cause of subfertility.”

The patient’s age must also be considered, she noted.

“A therapeutic switch may be favored in a woman over 35 years of age, for example, whose fertility is already impaired by age,” Ms. Hamroun said.

As for the mechanism that might explain the effects of NSAIDs on conception, Ms. Hamroun said that prostaglandins are essential to ovulation and embryo implantation and explained that NSAIDs may work against ovulation and result in poor implantation (miscarriage) by blocking prostaglandins.

She pointed out that her results are in line with the ACR’s recommendation to discontinue NSAID use during the preconception period in women with SpA who are having difficulty conceiving.
 

Control before conception is important

Sinead Maguire, MD, a clinical and research fellow in the Spondylitis Program at Toronto (Ont.) Western Hospital who was not part of the study, said the study highlights the importance of optimizing disease control before conception.

“There are a number of things rheumatologists can do to support our SpA patients when they are trying to conceive,” she told this news organization. “One of the most important issues to address is ensuring their SpA is in remission and continues to remain so. For that reason, if a woman is requiring regular NSAIDs for symptom control, the results of this study might encourage me to consider a biologic agent sooner to ensure remission.”

She urged women who want to become pregnant to discuss medications with their rheumatologist before trying to conceive.

“It is very exciting to see studies such as this so that rheumatologists can provide answers to our patients’ questions with evidence-based advice,” she said.

Ms. Hamroun and several coauthors had no disclosures. Other coauthors disclosed relationships with companies including Merck/MSD, Novartis, Janssen, AbbVie/Abbott, Amgen, AstraZeneca, Biogen, Bristol-Myers Squibb, Galapagos, Eli Lilly, Novartis, and/or UCB. Dr. Maguire reports no relevant financial relationships.

A version of this article first appeared on Medscape.com.

PHILADELPHIA – Women with spondyloarthritis (SpA) who are desiring pregnancy may want to consider decreasing use or discontinuing use (with supervision) of nonsteroidal anti-inflammatory drugs before conception, new data suggest.

Researchers have found a connection between NSAID use and age and a significantly longer time to conception among women with spondyloarthritis. Sabrina Hamroun, MMed, with the rheumatology department at the University Hospital Cochin, Paris, presented the findings during a press conference at the annual meeting of the American College of Rheumatology.

Subfertility group took an average of 16 months to get pregnant

Subfertility was observed in 40 (45.4%) of the women, with an average time to conception of 16.1 months. A woman was considered subfertile if her time to conception was more than 12 months or if she did not become pregnant.

The average preconception Bath Ankylosing Spondylitis Disease Activity Index score was 2.9 (+/- 2.1), the authors noted. The average age of the participants was 32 years.

Twenty-three patients were treated with NSAIDs, eight with corticosteroids, 12 with conventional synthetic disease-modifying antirheumatic drugs, and 61 with biologics.

Researchers adjusted for factors including age, body mass index, disease duration and severity, smoking, form of SpA (axial, peripheral, or both), and medication in the preconception period.

They found significant associations between longer time to conception and age (hazard ratio, 1.22; 95% confidence interval, 1.08-1.40; P < .001), and a much higher hazard ratio with the use of NSAIDs during preconception (HR, 3.01; 95% CI, 2.15-3.85; P = .01).

Some data unavailable

Ms. Hamroun acknowledged that no data were available on the frequency of sexual intercourse or quality of life, factors that could affect time to conception. Women were asked when they discontinued contraceptive use and actively began trying to become pregnant.

She stated that information on the dose of NSAIDs used by the patients was incomplete, noting, “We were therefore unable to adjust the results of our statistical analyses on the dose used by patients.”

Additionally, because the study participants were patients at tertiary centers in France and had more severe disease, the results may not be generalizable to all women of childbearing age. Patients with less severe SpA are often managed in outpatient settings in France, she said.

When asked about alternatives to NSAIDs, Ms. Hamroun said that anti–tumor necrosis factor agents with low placental passage may be a good alternative “if a woman with long-standing difficulties to conceive needs a regular use of NSAIDs to control disease activity, in the absence of any other cause of subfertility.”

The patient’s age must also be considered, she noted.

“A therapeutic switch may be favored in a woman over 35 years of age, for example, whose fertility is already impaired by age,” Ms. Hamroun said.

As for the mechanism that might explain the effects of NSAIDs on conception, Ms. Hamroun said that prostaglandins are essential to ovulation and embryo implantation and explained that NSAIDs may work against ovulation and result in poor implantation (miscarriage) by blocking prostaglandins.

She pointed out that her results are in line with the ACR’s recommendation to discontinue NSAID use during the preconception period in women with SpA who are having difficulty conceiving.
 

Control before conception is important

Sinead Maguire, MD, a clinical and research fellow in the Spondylitis Program at Toronto (Ont.) Western Hospital who was not part of the study, said the study highlights the importance of optimizing disease control before conception.

“There are a number of things rheumatologists can do to support our SpA patients when they are trying to conceive,” she told this news organization. “One of the most important issues to address is ensuring their SpA is in remission and continues to remain so. For that reason, if a woman is requiring regular NSAIDs for symptom control, the results of this study might encourage me to consider a biologic agent sooner to ensure remission.”

She urged women who want to become pregnant to discuss medications with their rheumatologist before trying to conceive.

“It is very exciting to see studies such as this so that rheumatologists can provide answers to our patients’ questions with evidence-based advice,” she said.

Ms. Hamroun and several coauthors had no disclosures. Other coauthors disclosed relationships with companies including Merck/MSD, Novartis, Janssen, AbbVie/Abbott, Amgen, AstraZeneca, Biogen, Bristol-Myers Squibb, Galapagos, Eli Lilly, Novartis, and/or UCB. Dr. Maguire reports no relevant financial relationships.

A version of this article first appeared on Medscape.com.

PHILADELPHIA – Women with spondyloarthritis (SpA) who are desiring pregnancy may want to consider decreasing use or discontinuing use (with supervision) of nonsteroidal anti-inflammatory drugs before conception, new data suggest.

Researchers have found a connection between NSAID use and age and a significantly longer time to conception among women with spondyloarthritis. Sabrina Hamroun, MMed, with the rheumatology department at the University Hospital Cochin, Paris, presented the findings during a press conference at the annual meeting of the American College of Rheumatology.

Subfertility group took an average of 16 months to get pregnant

Subfertility was observed in 40 (45.4%) of the women, with an average time to conception of 16.1 months. A woman was considered subfertile if her time to conception was more than 12 months or if she did not become pregnant.

The average preconception Bath Ankylosing Spondylitis Disease Activity Index score was 2.9 (+/- 2.1), the authors noted. The average age of the participants was 32 years.

Twenty-three patients were treated with NSAIDs, eight with corticosteroids, 12 with conventional synthetic disease-modifying antirheumatic drugs, and 61 with biologics.

Researchers adjusted for factors including age, body mass index, disease duration and severity, smoking, form of SpA (axial, peripheral, or both), and medication in the preconception period.

They found significant associations between longer time to conception and age (hazard ratio, 1.22; 95% confidence interval, 1.08-1.40; P < .001), and a much higher hazard ratio with the use of NSAIDs during preconception (HR, 3.01; 95% CI, 2.15-3.85; P = .01).

Some data unavailable

Ms. Hamroun acknowledged that no data were available on the frequency of sexual intercourse or quality of life, factors that could affect time to conception. Women were asked when they discontinued contraceptive use and actively began trying to become pregnant.

She stated that information on the dose of NSAIDs used by the patients was incomplete, noting, “We were therefore unable to adjust the results of our statistical analyses on the dose used by patients.”

Additionally, because the study participants were patients at tertiary centers in France and had more severe disease, the results may not be generalizable to all women of childbearing age. Patients with less severe SpA are often managed in outpatient settings in France, she said.

When asked about alternatives to NSAIDs, Ms. Hamroun said that anti–tumor necrosis factor agents with low placental passage may be a good alternative “if a woman with long-standing difficulties to conceive needs a regular use of NSAIDs to control disease activity, in the absence of any other cause of subfertility.”

The patient’s age must also be considered, she noted.

“A therapeutic switch may be favored in a woman over 35 years of age, for example, whose fertility is already impaired by age,” Ms. Hamroun said.

As for the mechanism that might explain the effects of NSAIDs on conception, Ms. Hamroun said that prostaglandins are essential to ovulation and embryo implantation and explained that NSAIDs may work against ovulation and result in poor implantation (miscarriage) by blocking prostaglandins.

She pointed out that her results are in line with the ACR’s recommendation to discontinue NSAID use during the preconception period in women with SpA who are having difficulty conceiving.
 

Control before conception is important

Sinead Maguire, MD, a clinical and research fellow in the Spondylitis Program at Toronto (Ont.) Western Hospital who was not part of the study, said the study highlights the importance of optimizing disease control before conception.

“There are a number of things rheumatologists can do to support our SpA patients when they are trying to conceive,” she told this news organization. “One of the most important issues to address is ensuring their SpA is in remission and continues to remain so. For that reason, if a woman is requiring regular NSAIDs for symptom control, the results of this study might encourage me to consider a biologic agent sooner to ensure remission.”

She urged women who want to become pregnant to discuss medications with their rheumatologist before trying to conceive.

“It is very exciting to see studies such as this so that rheumatologists can provide answers to our patients’ questions with evidence-based advice,” she said.

Ms. Hamroun and several coauthors had no disclosures. Other coauthors disclosed relationships with companies including Merck/MSD, Novartis, Janssen, AbbVie/Abbott, Amgen, AstraZeneca, Biogen, Bristol-Myers Squibb, Galapagos, Eli Lilly, Novartis, and/or UCB. Dr. Maguire reports no relevant financial relationships.

A version of this article first appeared on Medscape.com.

PHILADELPHIA – Combining the immunomodulatory agent mycophenolate with the antifibrotic pirfenidone led to more rapid improvement and showed a trend to be more effective than mycophenolate mofetil alone for treating the signs and symptoms of scleroderma-related interstitial lung disease, but the combination therapy came with an increase in side effects, according to results from the Scleroderma Lung Study III.

Dinesh Khanna, MBBS, MSc, of the University of Michigan, Ann Arbor, presented the results at the annual meeting of the American College of Rheumatology. He noted some problems with the study – namely its small size, enrolling only 51 patients, about one-third of its original goal. But he also said it showed a potential signal for efficacy and that the study itself could serve as a “template” for future studies of combination mycophenolate mofetil (MMF) plus pirfenidone therapy for scleroderma-related interstitial lung disease (SSc-ILD).

“The pirfenidone patients had quite a bit more GI side effects and photosensitivity, and those are known side effects,” Dr. Khanna said in an interview. “So the combination therapy had more side effects but trends to higher efficacy.”

The design of SLS-III, a phase 2 clinical trial, was a challenge, Dr. Khanna explained. The goal was to enroll 150 SSc-ILD patients who hadn’t had any previous treatment for their disease. Finding those patients proved difficult. “In fact, if you look at the recent history, 70% of the patients with early diffuse scleroderma are on MMF,” he said in his presentation. Compounding low study enrollment was the intervening COVID-19 pandemic, he added.
 

Testing a faster-acting combination

Nonetheless, the trial managed to enroll 27 patients in the combination therapy group and 24 in the MMF-plus-placebo group and compared their outcomes over 18 months. Study dosing was 1,500 mg MMF twice daily and pirfenidone 801 mg three times daily, titrated to the tolerable dose.

Despite the study’s being underpowered, Dr. Khanna said, it still reported some notable outcomes that merit further investigation. “I think what was intriguing in the study was the long-term benefit in the patient-reported outcomes and the structural changes,” he said in the interview.

Among those notable outcomes was a clinically significant change in forced vital capacity (FVC) percentage for the combination vs. the placebo groups: 2.24% vs. 2.09%. He also noted that the combination group saw a somewhat more robust improvement in FVC at six months: 2.59% (± 0.98%) vs. 0.92% (± 1.1%) in the placebo group.

The combination group showed greater improvements in high-resolution computed tomography-evaluated lung involvement and lung fibrosis and patient-reported outcomes, including a statistically significant 3.67-point greater improvement in PROMIS-29 physical function score (4.42 vs. 0.75).

The patients on combination therapy had higher rates of serious adverse events (SAEs), and seven discontinued one or both study drugs early, all in the combined arm. Four combination therapy patients had six SAEs, compared to two placebo patients with three SAEs. In the combination group, SAEs included chest pain, herpes zoster ophthalmicus, nodular basal cell cancer, marginal zone B cell lymphoma, renal crisis, and dyspnea. SAEs in the placebo group were colitis, COVID-19 and hypoxic respiratory failure.

Study design challenges

Nonetheless, Dr. Khanna said the SLS-III data are consistent with the SLS-II findings, with mean improvements in FVC of 2.24% and 2.1%, respectively.

“The next study may be able to replicate what we tried to do, keeping in mind that there are really no MMF-naive patients who are walking around,” Dr. Khanna said. “So the challenge is about the feasibility of recruiting within a trial vs. trying to show a statistical difference between the drug and placebo.”

This study could serve as a foundation for future studies of MMF in patients with SSc-ILD, Robert Spiera, MD, of the Hospital for Special Surgery in New York, said in an interview. “There are lessons to be learned both from the study but also from prior studies looking at MMF use in the background in patients treated with other drugs in clinical trials,” he said.

Dr. Spiera noted that the study had other challenges besides the difficulty in recruiting patients who hadn’t been on MMF therapy. “A great challenge is that the benefit with regard to the impact on the lungs from MMF seems most prominent in the first 6 months to a year to even 2 years that somebody is on the drug,” he said.

The other challenge with this study is that a large proportion of patients had limited systemic disease and relatively lower levels of skin disease compared with other studies of patients on MMF, Dr. Spiera said.

“The optimal treatment of scleroderma-associated lung disease remains a very important and not-adequately met need,” he said. “Particularly, we’re looking for drugs that are tolerable in a patient population that are very prone to GI side effects in general. This study and others have taught us a lot about trial design, and I think more globally this will allow us to move this field forward.”

Dr. Khanna disclosed relationships with Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, CSL Behring, Horizon Therapeutics USA, Janssen Global Services, Prometheus Biosciences, Mitsubishi Tanabe Pharma Corp., Genentech/Roche, Theraly, and Pfizer. Genentech provided funding for the study and pirfenidone and placebo drugs at no cost.

Dr. Spiera disclosed relationships with GlaxoSmithKline, Boehringer-Ingelheim, Corbus Pharmaceutical, InflaRx, AbbVie/Abbott, Sanofi, Novartis, Chemocentryx, Roche and Vera.

PHILADELPHIA – Combining the immunomodulatory agent mycophenolate with the antifibrotic pirfenidone led to more rapid improvement and showed a trend to be more effective than mycophenolate mofetil alone for treating the signs and symptoms of scleroderma-related interstitial lung disease, but the combination therapy came with an increase in side effects, according to results from the Scleroderma Lung Study III.

Dinesh Khanna, MBBS, MSc, of the University of Michigan, Ann Arbor, presented the results at the annual meeting of the American College of Rheumatology. He noted some problems with the study – namely its small size, enrolling only 51 patients, about one-third of its original goal. But he also said it showed a potential signal for efficacy and that the study itself could serve as a “template” for future studies of combination mycophenolate mofetil (MMF) plus pirfenidone therapy for scleroderma-related interstitial lung disease (SSc-ILD).

“The pirfenidone patients had quite a bit more GI side effects and photosensitivity, and those are known side effects,” Dr. Khanna said in an interview. “So the combination therapy had more side effects but trends to higher efficacy.”

The design of SLS-III, a phase 2 clinical trial, was a challenge, Dr. Khanna explained. The goal was to enroll 150 SSc-ILD patients who hadn’t had any previous treatment for their disease. Finding those patients proved difficult. “In fact, if you look at the recent history, 70% of the patients with early diffuse scleroderma are on MMF,” he said in his presentation. Compounding low study enrollment was the intervening COVID-19 pandemic, he added.
 

Testing a faster-acting combination

Nonetheless, the trial managed to enroll 27 patients in the combination therapy group and 24 in the MMF-plus-placebo group and compared their outcomes over 18 months. Study dosing was 1,500 mg MMF twice daily and pirfenidone 801 mg three times daily, titrated to the tolerable dose.

Despite the study’s being underpowered, Dr. Khanna said, it still reported some notable outcomes that merit further investigation. “I think what was intriguing in the study was the long-term benefit in the patient-reported outcomes and the structural changes,” he said in the interview.

Among those notable outcomes was a clinically significant change in forced vital capacity (FVC) percentage for the combination vs. the placebo groups: 2.24% vs. 2.09%. He also noted that the combination group saw a somewhat more robust improvement in FVC at six months: 2.59% (± 0.98%) vs. 0.92% (± 1.1%) in the placebo group.

The combination group showed greater improvements in high-resolution computed tomography-evaluated lung involvement and lung fibrosis and patient-reported outcomes, including a statistically significant 3.67-point greater improvement in PROMIS-29 physical function score (4.42 vs. 0.75).

The patients on combination therapy had higher rates of serious adverse events (SAEs), and seven discontinued one or both study drugs early, all in the combined arm. Four combination therapy patients had six SAEs, compared to two placebo patients with three SAEs. In the combination group, SAEs included chest pain, herpes zoster ophthalmicus, nodular basal cell cancer, marginal zone B cell lymphoma, renal crisis, and dyspnea. SAEs in the placebo group were colitis, COVID-19 and hypoxic respiratory failure.

Study design challenges

Nonetheless, Dr. Khanna said the SLS-III data are consistent with the SLS-II findings, with mean improvements in FVC of 2.24% and 2.1%, respectively.

“The next study may be able to replicate what we tried to do, keeping in mind that there are really no MMF-naive patients who are walking around,” Dr. Khanna said. “So the challenge is about the feasibility of recruiting within a trial vs. trying to show a statistical difference between the drug and placebo.”

This study could serve as a foundation for future studies of MMF in patients with SSc-ILD, Robert Spiera, MD, of the Hospital for Special Surgery in New York, said in an interview. “There are lessons to be learned both from the study but also from prior studies looking at MMF use in the background in patients treated with other drugs in clinical trials,” he said.

Dr. Spiera noted that the study had other challenges besides the difficulty in recruiting patients who hadn’t been on MMF therapy. “A great challenge is that the benefit with regard to the impact on the lungs from MMF seems most prominent in the first 6 months to a year to even 2 years that somebody is on the drug,” he said.

The other challenge with this study is that a large proportion of patients had limited systemic disease and relatively lower levels of skin disease compared with other studies of patients on MMF, Dr. Spiera said.

“The optimal treatment of scleroderma-associated lung disease remains a very important and not-adequately met need,” he said. “Particularly, we’re looking for drugs that are tolerable in a patient population that are very prone to GI side effects in general. This study and others have taught us a lot about trial design, and I think more globally this will allow us to move this field forward.”

Dr. Khanna disclosed relationships with Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, CSL Behring, Horizon Therapeutics USA, Janssen Global Services, Prometheus Biosciences, Mitsubishi Tanabe Pharma Corp., Genentech/Roche, Theraly, and Pfizer. Genentech provided funding for the study and pirfenidone and placebo drugs at no cost.

Dr. Spiera disclosed relationships with GlaxoSmithKline, Boehringer-Ingelheim, Corbus Pharmaceutical, InflaRx, AbbVie/Abbott, Sanofi, Novartis, Chemocentryx, Roche and Vera.

PHILADELPHIA – Combining the immunomodulatory agent mycophenolate with the antifibrotic pirfenidone led to more rapid improvement and showed a trend to be more effective than mycophenolate mofetil alone for treating the signs and symptoms of scleroderma-related interstitial lung disease, but the combination therapy came with an increase in side effects, according to results from the Scleroderma Lung Study III.

Dinesh Khanna, MBBS, MSc, of the University of Michigan, Ann Arbor, presented the results at the annual meeting of the American College of Rheumatology. He noted some problems with the study – namely its small size, enrolling only 51 patients, about one-third of its original goal. But he also said it showed a potential signal for efficacy and that the study itself could serve as a “template” for future studies of combination mycophenolate mofetil (MMF) plus pirfenidone therapy for scleroderma-related interstitial lung disease (SSc-ILD).

“The pirfenidone patients had quite a bit more GI side effects and photosensitivity, and those are known side effects,” Dr. Khanna said in an interview. “So the combination therapy had more side effects but trends to higher efficacy.”

The design of SLS-III, a phase 2 clinical trial, was a challenge, Dr. Khanna explained. The goal was to enroll 150 SSc-ILD patients who hadn’t had any previous treatment for their disease. Finding those patients proved difficult. “In fact, if you look at the recent history, 70% of the patients with early diffuse scleroderma are on MMF,” he said in his presentation. Compounding low study enrollment was the intervening COVID-19 pandemic, he added.
 

Testing a faster-acting combination

Nonetheless, the trial managed to enroll 27 patients in the combination therapy group and 24 in the MMF-plus-placebo group and compared their outcomes over 18 months. Study dosing was 1,500 mg MMF twice daily and pirfenidone 801 mg three times daily, titrated to the tolerable dose.

Despite the study’s being underpowered, Dr. Khanna said, it still reported some notable outcomes that merit further investigation. “I think what was intriguing in the study was the long-term benefit in the patient-reported outcomes and the structural changes,” he said in the interview.

Among those notable outcomes was a clinically significant change in forced vital capacity (FVC) percentage for the combination vs. the placebo groups: 2.24% vs. 2.09%. He also noted that the combination group saw a somewhat more robust improvement in FVC at six months: 2.59% (± 0.98%) vs. 0.92% (± 1.1%) in the placebo group.

The combination group showed greater improvements in high-resolution computed tomography-evaluated lung involvement and lung fibrosis and patient-reported outcomes, including a statistically significant 3.67-point greater improvement in PROMIS-29 physical function score (4.42 vs. 0.75).

The patients on combination therapy had higher rates of serious adverse events (SAEs), and seven discontinued one or both study drugs early, all in the combined arm. Four combination therapy patients had six SAEs, compared to two placebo patients with three SAEs. In the combination group, SAEs included chest pain, herpes zoster ophthalmicus, nodular basal cell cancer, marginal zone B cell lymphoma, renal crisis, and dyspnea. SAEs in the placebo group were colitis, COVID-19 and hypoxic respiratory failure.

Study design challenges

Nonetheless, Dr. Khanna said the SLS-III data are consistent with the SLS-II findings, with mean improvements in FVC of 2.24% and 2.1%, respectively.

“The next study may be able to replicate what we tried to do, keeping in mind that there are really no MMF-naive patients who are walking around,” Dr. Khanna said. “So the challenge is about the feasibility of recruiting within a trial vs. trying to show a statistical difference between the drug and placebo.”

This study could serve as a foundation for future studies of MMF in patients with SSc-ILD, Robert Spiera, MD, of the Hospital for Special Surgery in New York, said in an interview. “There are lessons to be learned both from the study but also from prior studies looking at MMF use in the background in patients treated with other drugs in clinical trials,” he said.

Dr. Spiera noted that the study had other challenges besides the difficulty in recruiting patients who hadn’t been on MMF therapy. “A great challenge is that the benefit with regard to the impact on the lungs from MMF seems most prominent in the first 6 months to a year to even 2 years that somebody is on the drug,” he said.

The other challenge with this study is that a large proportion of patients had limited systemic disease and relatively lower levels of skin disease compared with other studies of patients on MMF, Dr. Spiera said.

“The optimal treatment of scleroderma-associated lung disease remains a very important and not-adequately met need,” he said. “Particularly, we’re looking for drugs that are tolerable in a patient population that are very prone to GI side effects in general. This study and others have taught us a lot about trial design, and I think more globally this will allow us to move this field forward.”

Dr. Khanna disclosed relationships with Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, CSL Behring, Horizon Therapeutics USA, Janssen Global Services, Prometheus Biosciences, Mitsubishi Tanabe Pharma Corp., Genentech/Roche, Theraly, and Pfizer. Genentech provided funding for the study and pirfenidone and placebo drugs at no cost.

Dr. Spiera disclosed relationships with GlaxoSmithKline, Boehringer-Ingelheim, Corbus Pharmaceutical, InflaRx, AbbVie/Abbott, Sanofi, Novartis, Chemocentryx, Roche and Vera.

In my last column, I discussed blogging as a great way to capture the attention of anyone interested in your practice, especially prospective patients. If you are already blogging – or would like to consider a less crowded and competitive activity – podcasting might be the answer. At this writing (November 2022), more than 600 million blogs are online, compared with about 2 million podcasts, and relatively few of them are run by physicians. With podcasts, you have a better chance of standing out in a crowded online world.

Starting a podcast is not difficult, but there are several steps you need to go through before launching one.

As with blogging, start by outlining a long-range plan. Your general topic will probably be your specialty, but you will need to narrow your focus to a few specific subjects, such as the problems you see most often, or a subspecialty that you concentrate on. You can always expand your topic later, as you get more popular. Choose a name for your podcast, and purchase a domain name that accurately describes it.

You will also need to choose a hosting service. Numerous inexpensive hosting platforms are available, and a simple Google search will find them for you. Many of them provide free learning materials, helpful creative tools, and customer support to get you through the confusing technical aspects. They can also help you choose a music introduction (to add a bit of polish), and help you piece together your audio segments. Buzzsprout, RSS.com, and Podbean get good reviews on many sites. (As always, I have no financial interest in any company or service mentioned herein.)

Hosting services can assist you in creating a template – a framework that you can reuse each time you record an episode – containing your intro and exit music, tracks for your conversations, etc. This will make your podcasts instantly recognizable each time your listeners tune in.

Many podcasting experts recommend recruiting a co-host. This can be an associate within your practice, a friend who practices elsewhere, or perhaps a resident in an academic setting. You will be able to spread the workload of creating, editing, and promoting. Plus, it is much easier to generate interesting content when two people are having a conversation, rather than one person lecturing from a prepared script. You might also consider having multiple co-hosts, either to expand episodes into group discussions, or to take turns working with you in covering different subjects.

How long you make your podcast is entirely up to you. Some consultants recommend specific time frames, such as 5 minutes (because that’s an average attention span), or 28 minutes (because that’s the average driving commute time). There are short podcasts and long ones; whatever works for you is fine, as long as you don’t drift off the topic. Furthermore, no one says they must all be the same length; when you are finished talking, you are done. And no one says you must stick with one subject throughout. Combining several short segments might hold more listeners’ interest and will make it easier to share small clips on social media.

Content guidelines are similar to those for blogs. Give people content that will be of interest or benefit to them. Talk about subjects – medical and otherwise – that are relevant to your practice or are prominent in the news.

As with blogs, try to avoid polarizing political discussions, and while it’s fine to discuss treatments and procedures that you offer, aggressive solicitation tends to make viewers look elsewhere. Keep any medical advice in general terms; don’t portray any specific patients as examples.

When your podcast is ready, your hosting platform will show you how to submit it to iTunes, and how to submit your podcast RSS feed to other podcast directories. As you upload new episodes, your host will automatically update your RSS feed, so that any directory you are listed on will receive the new episode.

Once you are uploaded, you can use your host’s social sharing tools to spread the word. As with blogs, use social media, such as your practice’s Facebook page, to push podcast updates into patients’ feeds and track relevant Twitter hashtags to find online communities that might be interested in your subject matter. You should also find your episode embed code (which your host will have) and place it in a prominent place on your website so patients can listen directly from there.

Transcriptions are another excellent promotional tool. Search engines will “read” your podcasts and list them in searches. Some podcast hosts will do transcribing for a fee, but there are independent transcription services as well.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at dermnews@mdedge.com.

In my last column, I discussed blogging as a great way to capture the attention of anyone interested in your practice, especially prospective patients. If you are already blogging – or would like to consider a less crowded and competitive activity – podcasting might be the answer. At this writing (November 2022), more than 600 million blogs are online, compared with about 2 million podcasts, and relatively few of them are run by physicians. With podcasts, you have a better chance of standing out in a crowded online world.

Starting a podcast is not difficult, but there are several steps you need to go through before launching one.

As with blogging, start by outlining a long-range plan. Your general topic will probably be your specialty, but you will need to narrow your focus to a few specific subjects, such as the problems you see most often, or a subspecialty that you concentrate on. You can always expand your topic later, as you get more popular. Choose a name for your podcast, and purchase a domain name that accurately describes it.

You will also need to choose a hosting service. Numerous inexpensive hosting platforms are available, and a simple Google search will find them for you. Many of them provide free learning materials, helpful creative tools, and customer support to get you through the confusing technical aspects. They can also help you choose a music introduction (to add a bit of polish), and help you piece together your audio segments. Buzzsprout, RSS.com, and Podbean get good reviews on many sites. (As always, I have no financial interest in any company or service mentioned herein.)

Hosting services can assist you in creating a template – a framework that you can reuse each time you record an episode – containing your intro and exit music, tracks for your conversations, etc. This will make your podcasts instantly recognizable each time your listeners tune in.

Many podcasting experts recommend recruiting a co-host. This can be an associate within your practice, a friend who practices elsewhere, or perhaps a resident in an academic setting. You will be able to spread the workload of creating, editing, and promoting. Plus, it is much easier to generate interesting content when two people are having a conversation, rather than one person lecturing from a prepared script. You might also consider having multiple co-hosts, either to expand episodes into group discussions, or to take turns working with you in covering different subjects.

How long you make your podcast is entirely up to you. Some consultants recommend specific time frames, such as 5 minutes (because that’s an average attention span), or 28 minutes (because that’s the average driving commute time). There are short podcasts and long ones; whatever works for you is fine, as long as you don’t drift off the topic. Furthermore, no one says they must all be the same length; when you are finished talking, you are done. And no one says you must stick with one subject throughout. Combining several short segments might hold more listeners’ interest and will make it easier to share small clips on social media.

Content guidelines are similar to those for blogs. Give people content that will be of interest or benefit to them. Talk about subjects – medical and otherwise – that are relevant to your practice or are prominent in the news.

As with blogs, try to avoid polarizing political discussions, and while it’s fine to discuss treatments and procedures that you offer, aggressive solicitation tends to make viewers look elsewhere. Keep any medical advice in general terms; don’t portray any specific patients as examples.

When your podcast is ready, your hosting platform will show you how to submit it to iTunes, and how to submit your podcast RSS feed to other podcast directories. As you upload new episodes, your host will automatically update your RSS feed, so that any directory you are listed on will receive the new episode.

Once you are uploaded, you can use your host’s social sharing tools to spread the word. As with blogs, use social media, such as your practice’s Facebook page, to push podcast updates into patients’ feeds and track relevant Twitter hashtags to find online communities that might be interested in your subject matter. You should also find your episode embed code (which your host will have) and place it in a prominent place on your website so patients can listen directly from there.

Transcriptions are another excellent promotional tool. Search engines will “read” your podcasts and list them in searches. Some podcast hosts will do transcribing for a fee, but there are independent transcription services as well.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at dermnews@mdedge.com.

In my last column, I discussed blogging as a great way to capture the attention of anyone interested in your practice, especially prospective patients. If you are already blogging – or would like to consider a less crowded and competitive activity – podcasting might be the answer. At this writing (November 2022), more than 600 million blogs are online, compared with about 2 million podcasts, and relatively few of them are run by physicians. With podcasts, you have a better chance of standing out in a crowded online world.

Starting a podcast is not difficult, but there are several steps you need to go through before launching one.

As with blogging, start by outlining a long-range plan. Your general topic will probably be your specialty, but you will need to narrow your focus to a few specific subjects, such as the problems you see most often, or a subspecialty that you concentrate on. You can always expand your topic later, as you get more popular. Choose a name for your podcast, and purchase a domain name that accurately describes it.

You will also need to choose a hosting service. Numerous inexpensive hosting platforms are available, and a simple Google search will find them for you. Many of them provide free learning materials, helpful creative tools, and customer support to get you through the confusing technical aspects. They can also help you choose a music introduction (to add a bit of polish), and help you piece together your audio segments. Buzzsprout, RSS.com, and Podbean get good reviews on many sites. (As always, I have no financial interest in any company or service mentioned herein.)

Hosting services can assist you in creating a template – a framework that you can reuse each time you record an episode – containing your intro and exit music, tracks for your conversations, etc. This will make your podcasts instantly recognizable each time your listeners tune in.

Many podcasting experts recommend recruiting a co-host. This can be an associate within your practice, a friend who practices elsewhere, or perhaps a resident in an academic setting. You will be able to spread the workload of creating, editing, and promoting. Plus, it is much easier to generate interesting content when two people are having a conversation, rather than one person lecturing from a prepared script. You might also consider having multiple co-hosts, either to expand episodes into group discussions, or to take turns working with you in covering different subjects.

How long you make your podcast is entirely up to you. Some consultants recommend specific time frames, such as 5 minutes (because that’s an average attention span), or 28 minutes (because that’s the average driving commute time). There are short podcasts and long ones; whatever works for you is fine, as long as you don’t drift off the topic. Furthermore, no one says they must all be the same length; when you are finished talking, you are done. And no one says you must stick with one subject throughout. Combining several short segments might hold more listeners’ interest and will make it easier to share small clips on social media.

Content guidelines are similar to those for blogs. Give people content that will be of interest or benefit to them. Talk about subjects – medical and otherwise – that are relevant to your practice or are prominent in the news.

As with blogs, try to avoid polarizing political discussions, and while it’s fine to discuss treatments and procedures that you offer, aggressive solicitation tends to make viewers look elsewhere. Keep any medical advice in general terms; don’t portray any specific patients as examples.

When your podcast is ready, your hosting platform will show you how to submit it to iTunes, and how to submit your podcast RSS feed to other podcast directories. As you upload new episodes, your host will automatically update your RSS feed, so that any directory you are listed on will receive the new episode.

Once you are uploaded, you can use your host’s social sharing tools to spread the word. As with blogs, use social media, such as your practice’s Facebook page, to push podcast updates into patients’ feeds and track relevant Twitter hashtags to find online communities that might be interested in your subject matter. You should also find your episode embed code (which your host will have) and place it in a prominent place on your website so patients can listen directly from there.

Transcriptions are another excellent promotional tool. Search engines will “read” your podcasts and list them in searches. Some podcast hosts will do transcribing for a fee, but there are independent transcription services as well.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at dermnews@mdedge.com.

PHILADELPHIA – Aggressive therapy using conventional synthetic disease-modifying antirheumatic drugs (DMARDs) in combination with biologic agents early, soon after a child is diagnosed with polyarticular juvenile idiopathic arthritis (pJIA), enabled more patients to achieve clinical remission and longer times in inactive disease than more conventional therapeutic approaches, 3-year results of prospective, observational study demonstrated.

The results of The Childhood Arthritis and Rheumatology Research Alliance STOP-JIA study, which Yukiko Kimura, MD, presented at the annual meeting of the American College of Rheumatology, showed early combination therapy had benefits, compared with other treatment strategies that were more evident at 3 years than at 1 year of study.

“The STOP-JIA study showed that, after 3 years, patients who started a biologic early on in combination with methotrexate spent more time in inactive disease and achieved clinical remission more often when compared to those started on traditional step-up therapy,” Dr. Kimura, chief of pediatric rheumatology at Hackensack (N.J.) Meridian Health and professor of pediatrics at the Hackensack Meridian School of Medicine, said at a press conference. “This study shows that the treatment of poly-JIA patients receive initially very early on in their disease matters even 3 years after that treatment was started.”

The study compared three CARRA consensus treatment plans (CTP) for untreated pediatric pJIA patients: step-up (SU) – starting conventional synthetic DMARD therapy and adding a biologic if needed after 3 or more months; early-combination (EC) therapy – starting synthetic and biologic DMARDs together; and biologic first (BF) therapy – starting biologic DMARD monotherapy.

Dr. Kimura explained the rationale for the study. “Since biologic treatments were introduced more than 20 years ago, the prognosis for JIA significantly improved. These very effective medicines often work wonders, quickly reducing pain and inflammation in joint disease activity,” she said in the press conference. “What is not known, however, is when is the best time to start these very effective treatments.”

The most common approach is to start with a synthetic DMARD, typically methotrexate, and wait before starting a biologic, Dr. Kimura said.

“But even though methotrexate can work very well by itself, it does not work for every patient, and we don’t know whether waiting months for it to work and then starting a biologic might potentially lessen their effectiveness,” Dr. Kimura added. “We don’t know if there’s a window of opportunity that’s lost while waiting to see whether methotrexate will work.”

The study originally enrolled 400 patients, 297 of whom completed the 3-year visit – 190 in SU, 76 in EC and 31 in BF. At 12 months, the study found no statistically significant difference in clinically inactive disease (CID) between the groups, Dr. Kimura said.

Even at the 3-year visit, the percentage of patients in CID off glucocorticoids and clinical Juvenile Arthritis Disease Activity Score based on 10 joints inactive disease (cJADAS 10 ID) did not differ among the three groups, Dr. Kimura said in presenting the results. “But,” she added, “greater proportions of early-combination CTP group were able to achieve clinical remissions and spend more time with inactive disease in both CID and cJADAS 10.”

A closer look at the outcomes showed some separation between early-combination therapy and the other two treatment plans. The incidence of clinical remission (at any time point over 36 months) was 67.1% in the EC group vs. 49.1% and 47.3%, respectively, in the BF and SU groups, Dr. Kimura said. “The difference between the early-combination and step-up groups was highly significant [P = .007],” she added.

EC also had an edge in the percentage of time patients spent in CID (over 36 months): 39.2% versus 32% and 27.4%, respectively, in the BF and SU groups (P = .006 for EV vs. SU), as well as cJADAS 10 ID (50.6% in EC group vs. 42.8% and 37.5%, respectively in the BF and SU groups; P = .005 for EC vs. SU).

Dr. Kimura said that the STOP JIA trial will continue with longer-term analysis and ongoing monitoring of study patients through the CARRA registry. “These longer-term analyses and readouts will be important because even though the results at 12 months didn’t seem as definitive, it seems the longer we go, the more impact we see of the treatments that were started early on in this disease.”

The findings from this study are “significantly important,” Nina T. Washington, MD, MPH, a pediatric rheumatologist at the University of New Mexico Hospital, Albuquerque, and the Mary Bridge Children’s Hospital in Tacoma, Wash., said in an interview. “At least for the past decade we’ve really been advocating towards earlier and aggressive therapy, and that’s what this study shows: the sooner you can treat this disease, the sooner you can attack those joints that are inflamed, the better outcome you give the patient.”

The study also confirms that pediatric rheumatologists are not overtreating patients with pJIA, she added.

“In a sense we’re actually treating and preventing and if you have a child that has arthritis, it’s okay to treat that child,” Dr. Washington said. “For me that’s the most reassuring thing: that I’m not necessarily going overboard. If I have a child with polyarticular JIA and they have multiple inflamed joints and I have the evidence as they’re sitting in front of me, and I treat them. I’m going to give them the best outcome.”

The Patient Centered Outcomes Research Institute provided study funding. Dr. Kimura is chair of the CARRA JIA disease research committee and cochair of the CARRA Registry and Research Oversight Committee. She disclosed a financial relationship with Genentech. Dr. Washington has no relevant relationships to disclose.
 

PHILADELPHIA – Aggressive therapy using conventional synthetic disease-modifying antirheumatic drugs (DMARDs) in combination with biologic agents early, soon after a child is diagnosed with polyarticular juvenile idiopathic arthritis (pJIA), enabled more patients to achieve clinical remission and longer times in inactive disease than more conventional therapeutic approaches, 3-year results of prospective, observational study demonstrated.

The results of The Childhood Arthritis and Rheumatology Research Alliance STOP-JIA study, which Yukiko Kimura, MD, presented at the annual meeting of the American College of Rheumatology, showed early combination therapy had benefits, compared with other treatment strategies that were more evident at 3 years than at 1 year of study.

“The STOP-JIA study showed that, after 3 years, patients who started a biologic early on in combination with methotrexate spent more time in inactive disease and achieved clinical remission more often when compared to those started on traditional step-up therapy,” Dr. Kimura, chief of pediatric rheumatology at Hackensack (N.J.) Meridian Health and professor of pediatrics at the Hackensack Meridian School of Medicine, said at a press conference. “This study shows that the treatment of poly-JIA patients receive initially very early on in their disease matters even 3 years after that treatment was started.”

The study compared three CARRA consensus treatment plans (CTP) for untreated pediatric pJIA patients: step-up (SU) – starting conventional synthetic DMARD therapy and adding a biologic if needed after 3 or more months; early-combination (EC) therapy – starting synthetic and biologic DMARDs together; and biologic first (BF) therapy – starting biologic DMARD monotherapy.

Dr. Kimura explained the rationale for the study. “Since biologic treatments were introduced more than 20 years ago, the prognosis for JIA significantly improved. These very effective medicines often work wonders, quickly reducing pain and inflammation in joint disease activity,” she said in the press conference. “What is not known, however, is when is the best time to start these very effective treatments.”

The most common approach is to start with a synthetic DMARD, typically methotrexate, and wait before starting a biologic, Dr. Kimura said.

“But even though methotrexate can work very well by itself, it does not work for every patient, and we don’t know whether waiting months for it to work and then starting a biologic might potentially lessen their effectiveness,” Dr. Kimura added. “We don’t know if there’s a window of opportunity that’s lost while waiting to see whether methotrexate will work.”

The study originally enrolled 400 patients, 297 of whom completed the 3-year visit – 190 in SU, 76 in EC and 31 in BF. At 12 months, the study found no statistically significant difference in clinically inactive disease (CID) between the groups, Dr. Kimura said.

Even at the 3-year visit, the percentage of patients in CID off glucocorticoids and clinical Juvenile Arthritis Disease Activity Score based on 10 joints inactive disease (cJADAS 10 ID) did not differ among the three groups, Dr. Kimura said in presenting the results. “But,” she added, “greater proportions of early-combination CTP group were able to achieve clinical remissions and spend more time with inactive disease in both CID and cJADAS 10.”

A closer look at the outcomes showed some separation between early-combination therapy and the other two treatment plans. The incidence of clinical remission (at any time point over 36 months) was 67.1% in the EC group vs. 49.1% and 47.3%, respectively, in the BF and SU groups, Dr. Kimura said. “The difference between the early-combination and step-up groups was highly significant [P = .007],” she added.

EC also had an edge in the percentage of time patients spent in CID (over 36 months): 39.2% versus 32% and 27.4%, respectively, in the BF and SU groups (P = .006 for EV vs. SU), as well as cJADAS 10 ID (50.6% in EC group vs. 42.8% and 37.5%, respectively in the BF and SU groups; P = .005 for EC vs. SU).

Dr. Kimura said that the STOP JIA trial will continue with longer-term analysis and ongoing monitoring of study patients through the CARRA registry. “These longer-term analyses and readouts will be important because even though the results at 12 months didn’t seem as definitive, it seems the longer we go, the more impact we see of the treatments that were started early on in this disease.”

The findings from this study are “significantly important,” Nina T. Washington, MD, MPH, a pediatric rheumatologist at the University of New Mexico Hospital, Albuquerque, and the Mary Bridge Children’s Hospital in Tacoma, Wash., said in an interview. “At least for the past decade we’ve really been advocating towards earlier and aggressive therapy, and that’s what this study shows: the sooner you can treat this disease, the sooner you can attack those joints that are inflamed, the better outcome you give the patient.”

The study also confirms that pediatric rheumatologists are not overtreating patients with pJIA, she added.

“In a sense we’re actually treating and preventing and if you have a child that has arthritis, it’s okay to treat that child,” Dr. Washington said. “For me that’s the most reassuring thing: that I’m not necessarily going overboard. If I have a child with polyarticular JIA and they have multiple inflamed joints and I have the evidence as they’re sitting in front of me, and I treat them. I’m going to give them the best outcome.”

The Patient Centered Outcomes Research Institute provided study funding. Dr. Kimura is chair of the CARRA JIA disease research committee and cochair of the CARRA Registry and Research Oversight Committee. She disclosed a financial relationship with Genentech. Dr. Washington has no relevant relationships to disclose.
 

PHILADELPHIA – Aggressive therapy using conventional synthetic disease-modifying antirheumatic drugs (DMARDs) in combination with biologic agents early, soon after a child is diagnosed with polyarticular juvenile idiopathic arthritis (pJIA), enabled more patients to achieve clinical remission and longer times in inactive disease than more conventional therapeutic approaches, 3-year results of prospective, observational study demonstrated.

The results of The Childhood Arthritis and Rheumatology Research Alliance STOP-JIA study, which Yukiko Kimura, MD, presented at the annual meeting of the American College of Rheumatology, showed early combination therapy had benefits, compared with other treatment strategies that were more evident at 3 years than at 1 year of study.

“The STOP-JIA study showed that, after 3 years, patients who started a biologic early on in combination with methotrexate spent more time in inactive disease and achieved clinical remission more often when compared to those started on traditional step-up therapy,” Dr. Kimura, chief of pediatric rheumatology at Hackensack (N.J.) Meridian Health and professor of pediatrics at the Hackensack Meridian School of Medicine, said at a press conference. “This study shows that the treatment of poly-JIA patients receive initially very early on in their disease matters even 3 years after that treatment was started.”

The study compared three CARRA consensus treatment plans (CTP) for untreated pediatric pJIA patients: step-up (SU) – starting conventional synthetic DMARD therapy and adding a biologic if needed after 3 or more months; early-combination (EC) therapy – starting synthetic and biologic DMARDs together; and biologic first (BF) therapy – starting biologic DMARD monotherapy.

Dr. Kimura explained the rationale for the study. “Since biologic treatments were introduced more than 20 years ago, the prognosis for JIA significantly improved. These very effective medicines often work wonders, quickly reducing pain and inflammation in joint disease activity,” she said in the press conference. “What is not known, however, is when is the best time to start these very effective treatments.”

The most common approach is to start with a synthetic DMARD, typically methotrexate, and wait before starting a biologic, Dr. Kimura said.

“But even though methotrexate can work very well by itself, it does not work for every patient, and we don’t know whether waiting months for it to work and then starting a biologic might potentially lessen their effectiveness,” Dr. Kimura added. “We don’t know if there’s a window of opportunity that’s lost while waiting to see whether methotrexate will work.”

The study originally enrolled 400 patients, 297 of whom completed the 3-year visit – 190 in SU, 76 in EC and 31 in BF. At 12 months, the study found no statistically significant difference in clinically inactive disease (CID) between the groups, Dr. Kimura said.

Even at the 3-year visit, the percentage of patients in CID off glucocorticoids and clinical Juvenile Arthritis Disease Activity Score based on 10 joints inactive disease (cJADAS 10 ID) did not differ among the three groups, Dr. Kimura said in presenting the results. “But,” she added, “greater proportions of early-combination CTP group were able to achieve clinical remissions and spend more time with inactive disease in both CID and cJADAS 10.”

A closer look at the outcomes showed some separation between early-combination therapy and the other two treatment plans. The incidence of clinical remission (at any time point over 36 months) was 67.1% in the EC group vs. 49.1% and 47.3%, respectively, in the BF and SU groups, Dr. Kimura said. “The difference between the early-combination and step-up groups was highly significant [P = .007],” she added.

EC also had an edge in the percentage of time patients spent in CID (over 36 months): 39.2% versus 32% and 27.4%, respectively, in the BF and SU groups (P = .006 for EV vs. SU), as well as cJADAS 10 ID (50.6% in EC group vs. 42.8% and 37.5%, respectively in the BF and SU groups; P = .005 for EC vs. SU).

Dr. Kimura said that the STOP JIA trial will continue with longer-term analysis and ongoing monitoring of study patients through the CARRA registry. “These longer-term analyses and readouts will be important because even though the results at 12 months didn’t seem as definitive, it seems the longer we go, the more impact we see of the treatments that were started early on in this disease.”

The findings from this study are “significantly important,” Nina T. Washington, MD, MPH, a pediatric rheumatologist at the University of New Mexico Hospital, Albuquerque, and the Mary Bridge Children’s Hospital in Tacoma, Wash., said in an interview. “At least for the past decade we’ve really been advocating towards earlier and aggressive therapy, and that’s what this study shows: the sooner you can treat this disease, the sooner you can attack those joints that are inflamed, the better outcome you give the patient.”

The study also confirms that pediatric rheumatologists are not overtreating patients with pJIA, she added.

“In a sense we’re actually treating and preventing and if you have a child that has arthritis, it’s okay to treat that child,” Dr. Washington said. “For me that’s the most reassuring thing: that I’m not necessarily going overboard. If I have a child with polyarticular JIA and they have multiple inflamed joints and I have the evidence as they’re sitting in front of me, and I treat them. I’m going to give them the best outcome.”

The Patient Centered Outcomes Research Institute provided study funding. Dr. Kimura is chair of the CARRA JIA disease research committee and cochair of the CARRA Registry and Research Oversight Committee. She disclosed a financial relationship with Genentech. Dr. Washington has no relevant relationships to disclose.
 

This transcript has been edited for clarity.

Welcome to Impact Factor, your weekly dose of commentary on a new medical study. I’m Dr. F. Perry Wilson of the Yale School of Medicine.

On March 26, 2022, Hawaii became the last state in the United States to lift its indoor mask mandate. By the time the current school year started, there were essentially no public school mask mandates either.

Whether you viewed the mask as an emblem of stalwart defiance against a rampaging virus, or a scarlet letter emblematic of the overreaches of public policy, you probably aren’t seeing them much anymore.

And yet, the debate about masks still rages. Who was right, who was wrong? Who trusted science, and what does the science even say? If we brought our country into marriage counseling, would we be told it is time to move on?  To look forward, not backward? To plan for our bright future together?

Perhaps. But this question isn’t really moot just because masks have largely disappeared in the United States. Variants may emerge that lead to more infection waves – and other pandemics may occur in the future. And so I think it is important to discuss a study that, with quite rigorous analysis, attempts to answer the following question: Did masking in schools lower students’ and teachers’ risk of COVID?

We are talking about this study, appearing in the New England Journal of Medicine. The short version goes like this.

Researchers had access to two important sources of data. One – an accounting of all the teachers and students (more than 300,000 of them) in 79 public, noncharter school districts in Eastern Massachusetts who tested positive for COVID every week. Two – the date that each of those school districts lifted their mask mandates or (in the case of two districts) didn’t.

Right away, I’m sure you’re thinking of potential issues. Districts that kept masks even when the statewide ban was lifted are likely quite a bit different from districts that dropped masks right away. You’re right, of course – hold on to that thought; we’ll get there.

But first – the big question – would districts that kept their masks on longer do better when it comes to the rate of COVID infection?

When everyone was masking, COVID case rates were pretty similar. Statewide mandates are lifted in late February – and most school districts remove their mandates within a few weeks – the black line are the two districts (Boston and Chelsea) where mask mandates remained in place.

As time marched on, the case rates in the various districts spread out – with districts that kept masks on longer doing better than those that took them off, and districts that kept masks on the whole time doing best of all.

Prior to the mask mandate lifting, you see very similar COVID rates in districts that would eventually remove the mandate and those that would not, with a bit of noise around the initial Omicron wave which saw just a huge amount of people get infected.

And then, after the mandate was lifted, separation. Districts that held on to masks longer had lower rates of COVID infection.

In all, over the 15-weeks of the study, there were roughly 12,000 extra cases of COVID in the mask-free school districts, which corresponds to about 35% of the total COVID burden during that time. And, yes, kids do well with COVID – on average. But 12,000 extra cases is enough to translate into a significant number of important clinical outcomes – think hospitalizations and post-COVID syndromes. And of course, maybe most importantly, missed school days. Positive kids were not allowed in class no matter what district they were in.

Okay – I promised we’d address confounders. This was not a cluster-randomized trial, where some school districts had their mandates removed based on the vicissitudes of a virtual coin flip, as much as many of us would have been interested to see that. The decision to remove masks was up to the various school boards – and they had a lot of pressure on them from many different directions. But all we need to worry about is whether any of those things that pressure a school board to keep masks on would ALSO lead to fewer COVID cases. That’s how confounders work, and how you can get false results in a study like this.

And yes – districts that kept the masks on longer were different than those who took them right off. But check out how they were different.

The districts that kept masks on longer had more low-income students. More Black and Latino students. More students per classroom. These are all risk factors that increase the risk of COVID infection. In other words, the confounding here goes in the opposite direction of the results. If anything, these factors should make you more certain that masking works.

The authors also adjusted for other factors – the community transmission of COVID-19, vaccination rates, school district sizes, and so on. No major change in the results.

One concern I addressed to Dr. Ellie Murray, the biostatistician on the study – could districts that removed masks simply have been testing more to compensate, leading to increased capturing of cases?

If anything, the schools that kept masks on were testing more than the schools that took them off – again that would tend to imply that the results are even stronger than what was reported.

Is this a perfect study? Of course not – it’s one study, it’s from one state. And the relatively large effects from keeping masks on for one or 2 weeks require us to really embrace the concept of exponential growth of infections, but, if COVID has taught us anything, it is that small changes in initial conditions can have pretty big effects.

My daughter, who goes to a public school here in Connecticut, unmasked, was home with COVID this past week. She’s fine. But you know what? She missed a week of school. I worked from home to be with her – though I didn’t test positive. And that is a real cost to both of us that I think we need to consider when we consider the value of masks. Yes, they’re annoying – but if they keep kids in school, might they be worth it? Perhaps not for now, as cases aren’t surging. But in the future, be it a particularly concerning variant, or a whole new pandemic, we should not discount the simple, cheap, and apparently beneficial act of wearing masks to decrease transmission.

Dr. Perry Wilson is an associate professor of medicine and director of the Clinical and Translational Research Accelerator at Yale University, New Haven, Conn. He disclosed no relevant conflicts of interest.

A version of this article first appeared on Medscape.com.

This transcript has been edited for clarity.

Welcome to Impact Factor, your weekly dose of commentary on a new medical study. I’m Dr. F. Perry Wilson of the Yale School of Medicine.

On March 26, 2022, Hawaii became the last state in the United States to lift its indoor mask mandate. By the time the current school year started, there were essentially no public school mask mandates either.

Whether you viewed the mask as an emblem of stalwart defiance against a rampaging virus, or a scarlet letter emblematic of the overreaches of public policy, you probably aren’t seeing them much anymore.

And yet, the debate about masks still rages. Who was right, who was wrong? Who trusted science, and what does the science even say? If we brought our country into marriage counseling, would we be told it is time to move on?  To look forward, not backward? To plan for our bright future together?

Perhaps. But this question isn’t really moot just because masks have largely disappeared in the United States. Variants may emerge that lead to more infection waves – and other pandemics may occur in the future. And so I think it is important to discuss a study that, with quite rigorous analysis, attempts to answer the following question: Did masking in schools lower students’ and teachers’ risk of COVID?

We are talking about this study, appearing in the New England Journal of Medicine. The short version goes like this.

Researchers had access to two important sources of data. One – an accounting of all the teachers and students (more than 300,000 of them) in 79 public, noncharter school districts in Eastern Massachusetts who tested positive for COVID every week. Two – the date that each of those school districts lifted their mask mandates or (in the case of two districts) didn’t.

Right away, I’m sure you’re thinking of potential issues. Districts that kept masks even when the statewide ban was lifted are likely quite a bit different from districts that dropped masks right away. You’re right, of course – hold on to that thought; we’ll get there.

But first – the big question – would districts that kept their masks on longer do better when it comes to the rate of COVID infection?

When everyone was masking, COVID case rates were pretty similar. Statewide mandates are lifted in late February – and most school districts remove their mandates within a few weeks – the black line are the two districts (Boston and Chelsea) where mask mandates remained in place.

As time marched on, the case rates in the various districts spread out – with districts that kept masks on longer doing better than those that took them off, and districts that kept masks on the whole time doing best of all.

Prior to the mask mandate lifting, you see very similar COVID rates in districts that would eventually remove the mandate and those that would not, with a bit of noise around the initial Omicron wave which saw just a huge amount of people get infected.

And then, after the mandate was lifted, separation. Districts that held on to masks longer had lower rates of COVID infection.

In all, over the 15-weeks of the study, there were roughly 12,000 extra cases of COVID in the mask-free school districts, which corresponds to about 35% of the total COVID burden during that time. And, yes, kids do well with COVID – on average. But 12,000 extra cases is enough to translate into a significant number of important clinical outcomes – think hospitalizations and post-COVID syndromes. And of course, maybe most importantly, missed school days. Positive kids were not allowed in class no matter what district they were in.

Okay – I promised we’d address confounders. This was not a cluster-randomized trial, where some school districts had their mandates removed based on the vicissitudes of a virtual coin flip, as much as many of us would have been interested to see that. The decision to remove masks was up to the various school boards – and they had a lot of pressure on them from many different directions. But all we need to worry about is whether any of those things that pressure a school board to keep masks on would ALSO lead to fewer COVID cases. That’s how confounders work, and how you can get false results in a study like this.

And yes – districts that kept the masks on longer were different than those who took them right off. But check out how they were different.

The districts that kept masks on longer had more low-income students. More Black and Latino students. More students per classroom. These are all risk factors that increase the risk of COVID infection. In other words, the confounding here goes in the opposite direction of the results. If anything, these factors should make you more certain that masking works.

The authors also adjusted for other factors – the community transmission of COVID-19, vaccination rates, school district sizes, and so on. No major change in the results.

One concern I addressed to Dr. Ellie Murray, the biostatistician on the study – could districts that removed masks simply have been testing more to compensate, leading to increased capturing of cases?

If anything, the schools that kept masks on were testing more than the schools that took them off – again that would tend to imply that the results are even stronger than what was reported.

Is this a perfect study? Of course not – it’s one study, it’s from one state. And the relatively large effects from keeping masks on for one or 2 weeks require us to really embrace the concept of exponential growth of infections, but, if COVID has taught us anything, it is that small changes in initial conditions can have pretty big effects.

My daughter, who goes to a public school here in Connecticut, unmasked, was home with COVID this past week. She’s fine. But you know what? She missed a week of school. I worked from home to be with her – though I didn’t test positive. And that is a real cost to both of us that I think we need to consider when we consider the value of masks. Yes, they’re annoying – but if they keep kids in school, might they be worth it? Perhaps not for now, as cases aren’t surging. But in the future, be it a particularly concerning variant, or a whole new pandemic, we should not discount the simple, cheap, and apparently beneficial act of wearing masks to decrease transmission.

Dr. Perry Wilson is an associate professor of medicine and director of the Clinical and Translational Research Accelerator at Yale University, New Haven, Conn. He disclosed no relevant conflicts of interest.

A version of this article first appeared on Medscape.com.

This transcript has been edited for clarity.

Welcome to Impact Factor, your weekly dose of commentary on a new medical study. I’m Dr. F. Perry Wilson of the Yale School of Medicine.

On March 26, 2022, Hawaii became the last state in the United States to lift its indoor mask mandate. By the time the current school year started, there were essentially no public school mask mandates either.

Whether you viewed the mask as an emblem of stalwart defiance against a rampaging virus, or a scarlet letter emblematic of the overreaches of public policy, you probably aren’t seeing them much anymore.

And yet, the debate about masks still rages. Who was right, who was wrong? Who trusted science, and what does the science even say? If we brought our country into marriage counseling, would we be told it is time to move on?  To look forward, not backward? To plan for our bright future together?

Perhaps. But this question isn’t really moot just because masks have largely disappeared in the United States. Variants may emerge that lead to more infection waves – and other pandemics may occur in the future. And so I think it is important to discuss a study that, with quite rigorous analysis, attempts to answer the following question: Did masking in schools lower students’ and teachers’ risk of COVID?

We are talking about this study, appearing in the New England Journal of Medicine. The short version goes like this.

Researchers had access to two important sources of data. One – an accounting of all the teachers and students (more than 300,000 of them) in 79 public, noncharter school districts in Eastern Massachusetts who tested positive for COVID every week. Two – the date that each of those school districts lifted their mask mandates or (in the case of two districts) didn’t.

Right away, I’m sure you’re thinking of potential issues. Districts that kept masks even when the statewide ban was lifted are likely quite a bit different from districts that dropped masks right away. You’re right, of course – hold on to that thought; we’ll get there.

But first – the big question – would districts that kept their masks on longer do better when it comes to the rate of COVID infection?

When everyone was masking, COVID case rates were pretty similar. Statewide mandates are lifted in late February – and most school districts remove their mandates within a few weeks – the black line are the two districts (Boston and Chelsea) where mask mandates remained in place.

As time marched on, the case rates in the various districts spread out – with districts that kept masks on longer doing better than those that took them off, and districts that kept masks on the whole time doing best of all.

Prior to the mask mandate lifting, you see very similar COVID rates in districts that would eventually remove the mandate and those that would not, with a bit of noise around the initial Omicron wave which saw just a huge amount of people get infected.

And then, after the mandate was lifted, separation. Districts that held on to masks longer had lower rates of COVID infection.

In all, over the 15-weeks of the study, there were roughly 12,000 extra cases of COVID in the mask-free school districts, which corresponds to about 35% of the total COVID burden during that time. And, yes, kids do well with COVID – on average. But 12,000 extra cases is enough to translate into a significant number of important clinical outcomes – think hospitalizations and post-COVID syndromes. And of course, maybe most importantly, missed school days. Positive kids were not allowed in class no matter what district they were in.

Okay – I promised we’d address confounders. This was not a cluster-randomized trial, where some school districts had their mandates removed based on the vicissitudes of a virtual coin flip, as much as many of us would have been interested to see that. The decision to remove masks was up to the various school boards – and they had a lot of pressure on them from many different directions. But all we need to worry about is whether any of those things that pressure a school board to keep masks on would ALSO lead to fewer COVID cases. That’s how confounders work, and how you can get false results in a study like this.

And yes – districts that kept the masks on longer were different than those who took them right off. But check out how they were different.

The districts that kept masks on longer had more low-income students. More Black and Latino students. More students per classroom. These are all risk factors that increase the risk of COVID infection. In other words, the confounding here goes in the opposite direction of the results. If anything, these factors should make you more certain that masking works.

The authors also adjusted for other factors – the community transmission of COVID-19, vaccination rates, school district sizes, and so on. No major change in the results.

One concern I addressed to Dr. Ellie Murray, the biostatistician on the study – could districts that removed masks simply have been testing more to compensate, leading to increased capturing of cases?

If anything, the schools that kept masks on were testing more than the schools that took them off – again that would tend to imply that the results are even stronger than what was reported.

Is this a perfect study? Of course not – it’s one study, it’s from one state. And the relatively large effects from keeping masks on for one or 2 weeks require us to really embrace the concept of exponential growth of infections, but, if COVID has taught us anything, it is that small changes in initial conditions can have pretty big effects.

My daughter, who goes to a public school here in Connecticut, unmasked, was home with COVID this past week. She’s fine. But you know what? She missed a week of school. I worked from home to be with her – though I didn’t test positive. And that is a real cost to both of us that I think we need to consider when we consider the value of masks. Yes, they’re annoying – but if they keep kids in school, might they be worth it? Perhaps not for now, as cases aren’t surging. But in the future, be it a particularly concerning variant, or a whole new pandemic, we should not discount the simple, cheap, and apparently beneficial act of wearing masks to decrease transmission.

Dr. Perry Wilson is an associate professor of medicine and director of the Clinical and Translational Research Accelerator at Yale University, New Haven, Conn. He disclosed no relevant conflicts of interest.

A version of this article first appeared on Medscape.com.