Original Research

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Rapid response systems (RRSs) have been developed to identify and treat deteriorating patients on general hospital units.[1] The most commonly proposed approach to the problem of identifying and stabilizing deteriorating hospitalized patients includes some combination of an early warning system to detect the deterioration and an RRS to deal with it. We previously demonstrated that a relatively simple hospital‐specific prediction model employing routine laboratory values and vital sign data is capable of predicting clinical deterioration, the need for intensive care unit (ICU) transfer, and hospital mortality in patients admitted to general medicine units.[2, 3, 4, 5, 6]

Hospital readmissions within 30 days of hospital discharge occur often and are difficult to predict. Starting in 2013, readmission penalties have been applied to specific conditions in the United States (acute myocardial infarction, heart failure, and pneumonia), with the expectation that additional conditions will be added to this group in years to come.[7, 8] Unfortunately, interventions developed to date have not been universally successful in preventing hospital readmissions for various medical conditions and patient types.[9] One potential explanation for this is the inability to reliably predict which patients are at risk for readmission to better target preventative interventions. Predictors of hospital readmission can be disease specific, such as the presence of multivessel disease in patients hospitalized with myocardial infarction,[10] or more general, such as lack of available medical follow‐up postdischarge.[11] Therefore, we performed a study to determine whether the occurrence of automated clinical deterioration alerts (CDAs) predicted 30‐day hospital readmission.

METHODS

RESULTS

The final cohort had 3015 patients with a mean age of 57.5 17.5 years and 47.8% males. The most common reasons for hospital admission were infection or sepsis syndrome including pneumonia and urinary tract infections (23.6%), congestive heart failure or other cardiac conditions (18.4%), respiratory distress including chronic obstructive pulmonary disease (16.2%), acute or chronic renal failure (9.7%), gastrointestinal disorders (8.4%), and diabetes mellitus management (7.4%). Overall, there were 567 (18.8%) patients who were readmitted within 30 days of their hospital discharge date.

Table 2 shows the characteristics of patients readmitted within 30 days and of patients not requiring hospital readmission within 30 days. Patients requiring hospital readmission within 30 days were younger and had significantly more comorbidities as manifested by significantly greater Charlson scores and individual comorbidities including coronary artery disease, congestive heart disease, peripheral vascular disease, connective tissue disease, cirrhosis, diabetes mellitus with end‐organ complications, renal failure, and metastatic cancer. Patients with a 30‐day readmission had significantly longer duration of hospitalization, more emergency department visits in the 6 months prior to the index hospitalization, lower minimum hemoglobin measurements, higher minimum serum creatinine values, and were more likely to have Medicare or Medicaid insurance compared to patients without a 30‐day readmission.

There were 1141 (34.4%) patients that triggered a CDA. Patients triggering a CDA were significantly more likely to have a 30‐day readmission compared to those who did not trigger a CDA (23.6% vs 15.9%; P < 0.001). Patients triggering a CDA were also significantly more likely to be readmitted within 60 days (31.7% vs 22.1%; P < 0.001) and 90 days (35.8% vs 26.2%; P < 0.001) compared to patients who did not trigger a CDA. Multiple logistic regression identified the triggering of a CDA to be independently associated with 30‐day readmission (OR: 1.40; 95% CI: 1.26‐1.55; P = 0.001) (Table 3). Other independent predictors of 30‐day readmission were: an emergency department visit in the previous 6 months, increasing age in 1‐year increments, presence of connective tissue disease, diabetes mellitus with end‐organ complications, chronic renal disease, cirrhosis, and metastatic cancer (Hosmer‐Lemeshow goodness of fit test, 0.363). Figure 1 reveals the ROC curves for the logistic regression model (Table 3) with and without the CDA variable. As the ROC curves document, the 2 models had similar sensitivity for the entire range of specificities. Reflecting this, the area under the ROC curve for the model inclusive of the CDA variable equaled 0.675 (95% CI: 0.649‐0.700), whereas the area under the ROC curve for the model excluding the CDA variable equaled 0.658 (95% CI: 0.632‐0.684).

Figure 1

DISCUSSION

We demonstrated that the occurrence of an automated CDA is associated with increased risk for 30‐day hospital readmission. However, the addition of the CDA variable to the other variables identified to be independently associated with 30‐day readmission (Table 3) did not significantly add to the overall predictive accuracy of the derived logistic regression model. Other investigators have previously attempted to develop automated predictors of hospital readmission. Amarasingham et al. developed a real‐time electronic predictive model that identifies hospitalized heart failure patients at high risk for readmission or death from clinical and nonclinical risk factors present on admission.[13] Their electronic model demonstrated good discrimination for 30‐day mortality and readmission and performed as well, or better than, models developed by the Center for Medicaid and Medicare Services and the Acute Decompensated Heart Failure Registry. Similarly, Baillie et al. developed an automated prediction model that was effectively integrated into an existing EMR and identified patients on admission who were at risk for readmission within 30 days of discharge.[14] Our automated CDA differs from these previous risk predictors by surveying patients throughout their hospital stay as opposed to identifying risk for readmission at a single time point.

Several limitations of our study should be recognized. First, this was a noninterventional study aimed at examining the ability of CDAs to predict hospital readmission. Future studies are needed to assess whether the use of enhanced readmission prediction algorithms can be utilized to avert hospital readmissions. Second, the data derive from a single center, and this necessarily limits the generalizability of our findings. As such, our results may not reflect what one might see at other institutions. For example, Barnes‐Jewish Hospital has a regional referral pattern that includes community hospitals, regional long‐term acute care hospitals, nursing homes, and chronic wound, dialysis, and infusion clinics. This may explain, in part, the relatively high rate of hospital readmission observed in our cohort. Third, there is the possibility that CDAs were associated with readmission by chance given the number of potential predictor variables examined. The importance of CDAs as a determinant of rehospitalization requires confirmation in other independent populations. Fourth, it is likely that we did not capture all hospital readmissions, primarily those occurring outside of our hospital system. Therefore, we may have underestimated the actual rates of readmission for this cohort. Finally, we cannot be certain that all important predictors of hospital readmission were captured in this study.

The development of an accurate real‐time early warning system has the potential to identify patients at risk for various adverse outcomes including clinical deterioration, hospital death, and postdischarge readmission. By identifying patients at greatest risk for readmission, valuable healthcare resources can be better targeted to such populations. Our findings suggest that existing readmission predictors may suboptimally risk‐stratify patients, and it may be important to include additional clinical variables if pay for performance and other across‐institution comparisons are to be fair to institutions that care for more seriously ill patients. The variables identified as predictors of 30‐day hospital readmission in our study, with the exception of a CDA, are all readily identifiable clinical characteristics. The modest incremental value of a CDA to these clinical characteristics suggests that they would suffice for the identification of patients at high risk for hospital readmission. This is especially important for safety‐net institutions not routinely employing automated CDAs. These safety‐net hospitals provide a disproportionate level of care for patients who otherwise would have difficulty obtaining inpatient medical care and disproportionately carry the greatest burden of hospital readmissions.[15]

Geriatric syndromes are common clinical conditions in older adults that do not fall into specific disease categories. Unlike the traditional definition of a syndrome, geriatric syndrome refers to a condition that is mediated by multiple shared underlying risk factors.[1, 2] Conditions commonly referred to as geriatric syndromes include delirium, cognitive impairment, falls, unintentional weight loss, depressive symptoms, and incontinence. Even though many perceive it as medical misnomer,[3] geriatric syndromes have been shown to negatively impact quality of life and activities of daily living in older adults.[2] They are also associated with adverse outcomes such as increased healthcare utilization, functional decline, and mortality, even after adjusting for age and disease severity.[4, 5, 6] Hospitalized older adults, including those discharged to skilled nursing facilities (SNFs)[7, 8] are particularly at high risk for new‐onset or exacerbation of geriatric syndromes and poor outcomes.[7, 9, 10] However, hospital providers seldom assess, manage, or document geriatric syndromes because they are often overshadowed by disease conditions that lead to an acute episode requiring hospitalization (e.g., heart disease).[11]

Pharmacotherapy is the cornerstone of hospital treatment, and it is well‐known that it affects multiple physiologic systems causing side effects apart from the condition they are approved to treat. Given that geriatric syndromes are a result of impairments in multiple organ systems, it is plausible that pharmacotherapy may initiate or worsen these syndromes.[12] Medication‐related problems in older adults are well known. Polypharmacy and adverse drug events (as a result of drug‐drug/disease interactions and changes in pharmacokinetics and pharmacodynamics) are prevalent in multimorbid elderly patients.[13, 14, 15, 16] The prescribing cascade[17] increases the medication burden and may be a contributing factor for geriatric syndromes in hospitalized patients.[18] For instance, laxatives may be prescribed to counteract constipation caused by anticholinergic drugs.

The American Geriatric Society (AGS) Beers list[19, 20] and similar criteria[21] provide excellent resources to identify medications with potentially harmful interactions or adverse effects in older adults. Although these lists include medicines associated with a specific geriatric syndrome, they were not developed to explicitly link medicines across multiple geriatric syndromes, regardless of indication or appropriateness. For example, medications that effect important geriatric syndromes like unintentional weight/appetite loss, depression, and urinary incontinence are not extensively covered. In addition, disease‐appropriate medications (eg, ‐blockers for systolic heart failure), that may be associated with a geriatric syndrome (eg, falls) are not included; however, they may be important to consider for a patient and clinician who are weighing the disease benefits compared to the geriatric syndrome‐related risks. Finally, the AGS 2015 Beers criteria panel mentions the limitation that many medication associations may be excluded because older adults are less represented in clinical trials.[20] Clinicians are currently limited in identifying medications potentially contributing to a broad set of geriatric syndromes in their patients without a specific list of medications associated with geriatric syndromes (MAGS).[20]

In response to this gap, identifying these medications is important and should be a starting point in efforts toward prevention and treatment of geriatric syndromes. The 2 main objectives of this study were to first identify medications that may meaningfully contribute to 6 geriatric syndromes and subsequently describe the frequency of these medications in a population transitioning from acute care to postacute care to highlight the need and potential impact of such a list.

METHODS

This study included 2 phases that aligned with our 2 primary objectives. Phase 1 involved identifying medications associated with 6 geriatric syndromes, and phase 2 included a cross‐sectional analysis of the prevalence of these medications in a sample of patients discharged to SNFs.

Phase 1: Development of the MAGS List

Figure 1 depicts the underlying conceptual model and approach that was used in phase 1. The interaction between the patient factors and medication leads to polypharmacy that contributes to geriatric syndromes and additional adverse outcomes. As a starting point for mitigating geriatric syndromes, we used an iterative analytical process to identify a list of medications associated with the following geriatric syndromes that were documented to be highly prevalent in patients discharged to SNFs: cognitive impairment, delirium, falls, unintentional weight and/or appetite loss, urinary incontinence, and depression.[8] To be inclusive and sensitive, our approach differed from traditional systematic reviews, and in fact was meant to bring together much of the established systematic literature about disparate geriatric syndromes in 1 place, because patients often do not experience a geriatric syndrome in isolation, but rather experience multiple geriatric syndromes.[8] The MAGS list had 3 main inclusion criteria (Figure 1): (1) evidence in the published literature (systematic reviews, cohort studies, randomized clinical trials) that the medication is related to the syndrome, (2) expert panel opinion, and (3) drug databases (Lexicomp Online database[22] and/or US Food and Drug Administration [FDA]approved package inserts).[23] We generated an initial list of medications based on these 3 main criteria to identify medications with significant associations to each geriatric syndrome. The list was further expanded and vetted using an iterative review of each medication list as it related to each geriatric syndrome through a series of group meetings focused around each geriatric syndrome. Following further discussion, we obtained agreement among all team members for medications included in the final list. For each geriatric syndrome, we excluded medications from consideration if they were used to treat the same geriatric syndrome (eg, ‐adrenergic blockers used to treat incontinence in men were listed as associated with incontinence only in women). We classified medications according to the Established Pharmacologic Class available at the FDA website. We also compared our final MAGS list with the 2015 AGS Beer's list[20] by identifying medications that were related to the 6 geriatric syndromes. This included Beers[20]‐cited rationale of anticholinergic, extrapyramidal symptoms, orthostatic hypotension (eg, falls), high‐risk adverse central nervous system effects, sedating, cognitive decline (eg, antipsychotics), delirium, falls, fractures, incontinence, and gastrointestinal (eg, nausea, vomiting). Specifically, we assessed whether the medications were included as inappropriate by the AGS Beers 2015[20] list and also whether they documented the syndrome association for that medication.

Figure 1

RESULTS

DISCUSSION

An iterative process of evidence review by a multidisciplinary panel resulted in a list of 513 medications associated with 6 common geriatric syndromes. This analysis demonstrated that hospitalized, older patients discharged to SNFs were frequently prescribed MAGS. The rate of prescribing ranged from 100% of patients with a medication associated with falls to 36% for unintentional weight loss. Moreover, an alarming 43% of all medications at hospital discharge were MAGS. For this vulnerable population, the combination of high prevalence of MAGS and high risk of geriatric syndromes emphasize a need to critically review the risks and benefits of MAGS throughout hospitalization and at the time of discharge.

A body of evidence demonstrates that many drugs in a typical older adult regimen have no specific clinical indication, are considered inappropriate, or have uncertain efficacy in the geriatric population.[24, 25, 26] This study builds on the foundational work described in landmark reviews such as the AGS Beers[20] and STOPP/START[21] (Screening Tool of Older Persons' Potentially Inappropriate Prescriptions/Screening Tool to Alert doctors to Right, i.e. appropriate indicated Treatment) criteria. Both of these tools, however, were specifically designed as screening tools to identify medications considered unsafe for older adults under most circumstances and within specific illness states.[19, 20, 21] They are most often utilized when starting a medication to avoid acute adverse events. In contrast, the MAGS list was developed to be inclusive of medications that are often appropriate for many medical diagnoses but may also contribute to underlying geriatric syndromes that are more chronic in nature. In addition, inclusion of such medicines increases the sensitivity of screening for medications that can be targeted through patient‐centered deprescribing efforts when clinically appropriate.

A major strength of this study is that we bring together evidence across a spectrum of geriatric syndromes commonly experienced by hospitalized elders. In addition to evaluating multiple syndromes, we applied multiple modalities; particularly the use of an iterative review process by a multidisciplinary team of experts and using Lexicomp and FDA insert packages for linking medications to specific geriatric conditions. The inclusion criteria were broadened beyond single sources of evidence in an effort to capture a comprehensive list of medications. As a result, the MAGS list can be implemented as a screening tool for deprescribing interventions and assessing medication appropriateness to address individual or clusters of geriatric syndromes within a patient.

In addition to expanding this knowledge base, clinical relevance of the MAGS list is highlighted by its application to a sample of hospitalized older adults discharged to SNFs, a cohort known to experience geriatric syndromes. In fact, 43% of patients' medications at hospital discharge were MAGS. Importantly, due to the cross‐sectional nature of this study, we cannot be certain if the medication caused or potentiated each of the geriatric syndromes. However, hospitals and SNFs are devoting major resources toward reduction of falls, avoidance of urinary catheter use, and reduction of preventable readmissions. These efforts can be complemented by considering the number of medications associated with falls, urinary incontinence, and overall MAGS burden. The striking prevalence of MAGS demonstrates a rigorous need to weigh the risks and benefits of these medications. Above all, the intent of this study is not to propose that any MAGS be reflexively stopped, but rather that the MAGS list should facilitate a holistic approach to care for the complex older adult. For example, standard therapies such as gabapentin may be appropriate for treating neuralgic pain but may also contribute to falls and urinary incontinence. Thus, alternative pain treatments could be selected in place of gabapentin for a 75‐year old patient who is experiencing recurrent falls and increasing incontinence. Therefore, the MAGS list enables a patient‐provider discussion wherein medications' therapeutic benefits can be weighed against risks posed by specific clusters of geriatric syndromes, potential impact on quality of life, and consistency with goals of care.

This study has some limitations. First, although we examined a broad number of geriatric syndromes, several other geriatric syndromes experienced by hospitalized older adults were not addressed including: fecal incontinence, insomnia, and functional impairment. These syndromes were intentionally excluded from the study a priori due to reasons of feasibility and scope. Second, unlike the Beer's 2015 criteria, the MAGS list does not sub‐classify associations of medications with geriatric syndromes for patients with specific diseases (eg, heart failure). In fact, our MAGS list included medications often indicated in treating these diagnoses. A clinician must work with the patient to weigh the disease‐specific benefits of some medications with the potential effect on geriatric syndrome symptoms and outcomes. Third, the instrument has a very high sensitivity, which was intended to generate an inclusive list of medications that enables providers to weigh risks of geriatric syndromes with the intended indication benefit. The objective is not to use this list as a reflexive tool but rather help clinicians identify a starting point to address geriatric syndromes in their patients to make patient‐centered medication decisions. Although the MAGS list is intentionally large (sensitive), the advent of advanced bioinformatics can enable MAGS to be assessed in the future for both clinical and research purposes. Fourth, FDA insert packages and Lexicomp databases report anything experienced by the patient while on the particular medication, but it might not necessarily imply a causative link. The high use of MAGS and the specific geriatric syndrome may coexist due to the high prevalence and interplay of multimorbidity, polypharmacy, and geriatric syndromes in this population. Last, the list was developed by expert panel members predominantly from a single institution, which may introduce bias. Despite these limitations, the prevalence of these medications in a sample of patients transitioning from acute to postacute care highlights the utility of the MAGS list in future clinical research and quality improvement endeavors.

In conclusion, the MAGS list provides a comprehensive and sensitive indicator of medications associated with any of 6 geriatric syndromes regardless of medication indication and appropriateness. The MAGS list provides an overall degree of medication burden with respect to geriatric syndromes and a foundation for future research to assess the relationship between the presence of geriatric syndromes and syndrome‐associated medications. The MAGS list is an important first step in summarizing the data that link medications to geriatric syndromes. Future studies are needed to broaden the analysis of MAGS for other common geriatric syndromes and to identify new and emerging medications not present during the time of this analysis. The MAGS list has the potential to facilitate deprescribing efforts needed to combat the epidemic of overprescribing that may be contributing to the burden of geriatric syndromes among older patients.

Geriatric syndromes are common clinical conditions in older adults that do not fall into specific disease categories. Unlike the traditional definition of a syndrome, geriatric syndrome refers to a condition that is mediated by multiple shared underlying risk factors.[1, 2] Conditions commonly referred to as geriatric syndromes include delirium, cognitive impairment, falls, unintentional weight loss, depressive symptoms, and incontinence. Even though many perceive it as medical misnomer,[3] geriatric syndromes have been shown to negatively impact quality of life and activities of daily living in older adults.[2] They are also associated with adverse outcomes such as increased healthcare utilization, functional decline, and mortality, even after adjusting for age and disease severity.[4, 5, 6] Hospitalized older adults, including those discharged to skilled nursing facilities (SNFs)[7, 8] are particularly at high risk for new‐onset or exacerbation of geriatric syndromes and poor outcomes.[7, 9, 10] However, hospital providers seldom assess, manage, or document geriatric syndromes because they are often overshadowed by disease conditions that lead to an acute episode requiring hospitalization (e.g., heart disease).[11]

Pharmacotherapy is the cornerstone of hospital treatment, and it is well‐known that it affects multiple physiologic systems causing side effects apart from the condition they are approved to treat. Given that geriatric syndromes are a result of impairments in multiple organ systems, it is plausible that pharmacotherapy may initiate or worsen these syndromes.[12] Medication‐related problems in older adults are well known. Polypharmacy and adverse drug events (as a result of drug‐drug/disease interactions and changes in pharmacokinetics and pharmacodynamics) are prevalent in multimorbid elderly patients.[13, 14, 15, 16] The prescribing cascade[17] increases the medication burden and may be a contributing factor for geriatric syndromes in hospitalized patients.[18] For instance, laxatives may be prescribed to counteract constipation caused by anticholinergic drugs.

The American Geriatric Society (AGS) Beers list[19, 20] and similar criteria[21] provide excellent resources to identify medications with potentially harmful interactions or adverse effects in older adults. Although these lists include medicines associated with a specific geriatric syndrome, they were not developed to explicitly link medicines across multiple geriatric syndromes, regardless of indication or appropriateness. For example, medications that effect important geriatric syndromes like unintentional weight/appetite loss, depression, and urinary incontinence are not extensively covered. In addition, disease‐appropriate medications (eg, ‐blockers for systolic heart failure), that may be associated with a geriatric syndrome (eg, falls) are not included; however, they may be important to consider for a patient and clinician who are weighing the disease benefits compared to the geriatric syndrome‐related risks. Finally, the AGS 2015 Beers criteria panel mentions the limitation that many medication associations may be excluded because older adults are less represented in clinical trials.[20] Clinicians are currently limited in identifying medications potentially contributing to a broad set of geriatric syndromes in their patients without a specific list of medications associated with geriatric syndromes (MAGS).[20]

In response to this gap, identifying these medications is important and should be a starting point in efforts toward prevention and treatment of geriatric syndromes. The 2 main objectives of this study were to first identify medications that may meaningfully contribute to 6 geriatric syndromes and subsequently describe the frequency of these medications in a population transitioning from acute care to postacute care to highlight the need and potential impact of such a list.

METHODS

This study included 2 phases that aligned with our 2 primary objectives. Phase 1 involved identifying medications associated with 6 geriatric syndromes, and phase 2 included a cross‐sectional analysis of the prevalence of these medications in a sample of patients discharged to SNFs.

Phase 1: Development of the MAGS List

Figure 1 depicts the underlying conceptual model and approach that was used in phase 1. The interaction between the patient factors and medication leads to polypharmacy that contributes to geriatric syndromes and additional adverse outcomes. As a starting point for mitigating geriatric syndromes, we used an iterative analytical process to identify a list of medications associated with the following geriatric syndromes that were documented to be highly prevalent in patients discharged to SNFs: cognitive impairment, delirium, falls, unintentional weight and/or appetite loss, urinary incontinence, and depression.[8] To be inclusive and sensitive, our approach differed from traditional systematic reviews, and in fact was meant to bring together much of the established systematic literature about disparate geriatric syndromes in 1 place, because patients often do not experience a geriatric syndrome in isolation, but rather experience multiple geriatric syndromes.[8] The MAGS list had 3 main inclusion criteria (Figure 1): (1) evidence in the published literature (systematic reviews, cohort studies, randomized clinical trials) that the medication is related to the syndrome, (2) expert panel opinion, and (3) drug databases (Lexicomp Online database[22] and/or US Food and Drug Administration [FDA]approved package inserts).[23] We generated an initial list of medications based on these 3 main criteria to identify medications with significant associations to each geriatric syndrome. The list was further expanded and vetted using an iterative review of each medication list as it related to each geriatric syndrome through a series of group meetings focused around each geriatric syndrome. Following further discussion, we obtained agreement among all team members for medications included in the final list. For each geriatric syndrome, we excluded medications from consideration if they were used to treat the same geriatric syndrome (eg, ‐adrenergic blockers used to treat incontinence in men were listed as associated with incontinence only in women). We classified medications according to the Established Pharmacologic Class available at the FDA website. We also compared our final MAGS list with the 2015 AGS Beer's list[20] by identifying medications that were related to the 6 geriatric syndromes. This included Beers[20]‐cited rationale of anticholinergic, extrapyramidal symptoms, orthostatic hypotension (eg, falls), high‐risk adverse central nervous system effects, sedating, cognitive decline (eg, antipsychotics), delirium, falls, fractures, incontinence, and gastrointestinal (eg, nausea, vomiting). Specifically, we assessed whether the medications were included as inappropriate by the AGS Beers 2015[20] list and also whether they documented the syndrome association for that medication.

Figure 1

RESULTS

DISCUSSION

An iterative process of evidence review by a multidisciplinary panel resulted in a list of 513 medications associated with 6 common geriatric syndromes. This analysis demonstrated that hospitalized, older patients discharged to SNFs were frequently prescribed MAGS. The rate of prescribing ranged from 100% of patients with a medication associated with falls to 36% for unintentional weight loss. Moreover, an alarming 43% of all medications at hospital discharge were MAGS. For this vulnerable population, the combination of high prevalence of MAGS and high risk of geriatric syndromes emphasize a need to critically review the risks and benefits of MAGS throughout hospitalization and at the time of discharge.

A body of evidence demonstrates that many drugs in a typical older adult regimen have no specific clinical indication, are considered inappropriate, or have uncertain efficacy in the geriatric population.[24, 25, 26] This study builds on the foundational work described in landmark reviews such as the AGS Beers[20] and STOPP/START[21] (Screening Tool of Older Persons' Potentially Inappropriate Prescriptions/Screening Tool to Alert doctors to Right, i.e. appropriate indicated Treatment) criteria. Both of these tools, however, were specifically designed as screening tools to identify medications considered unsafe for older adults under most circumstances and within specific illness states.[19, 20, 21] They are most often utilized when starting a medication to avoid acute adverse events. In contrast, the MAGS list was developed to be inclusive of medications that are often appropriate for many medical diagnoses but may also contribute to underlying geriatric syndromes that are more chronic in nature. In addition, inclusion of such medicines increases the sensitivity of screening for medications that can be targeted through patient‐centered deprescribing efforts when clinically appropriate.

A major strength of this study is that we bring together evidence across a spectrum of geriatric syndromes commonly experienced by hospitalized elders. In addition to evaluating multiple syndromes, we applied multiple modalities; particularly the use of an iterative review process by a multidisciplinary team of experts and using Lexicomp and FDA insert packages for linking medications to specific geriatric conditions. The inclusion criteria were broadened beyond single sources of evidence in an effort to capture a comprehensive list of medications. As a result, the MAGS list can be implemented as a screening tool for deprescribing interventions and assessing medication appropriateness to address individual or clusters of geriatric syndromes within a patient.

In addition to expanding this knowledge base, clinical relevance of the MAGS list is highlighted by its application to a sample of hospitalized older adults discharged to SNFs, a cohort known to experience geriatric syndromes. In fact, 43% of patients' medications at hospital discharge were MAGS. Importantly, due to the cross‐sectional nature of this study, we cannot be certain if the medication caused or potentiated each of the geriatric syndromes. However, hospitals and SNFs are devoting major resources toward reduction of falls, avoidance of urinary catheter use, and reduction of preventable readmissions. These efforts can be complemented by considering the number of medications associated with falls, urinary incontinence, and overall MAGS burden. The striking prevalence of MAGS demonstrates a rigorous need to weigh the risks and benefits of these medications. Above all, the intent of this study is not to propose that any MAGS be reflexively stopped, but rather that the MAGS list should facilitate a holistic approach to care for the complex older adult. For example, standard therapies such as gabapentin may be appropriate for treating neuralgic pain but may also contribute to falls and urinary incontinence. Thus, alternative pain treatments could be selected in place of gabapentin for a 75‐year old patient who is experiencing recurrent falls and increasing incontinence. Therefore, the MAGS list enables a patient‐provider discussion wherein medications' therapeutic benefits can be weighed against risks posed by specific clusters of geriatric syndromes, potential impact on quality of life, and consistency with goals of care.

This study has some limitations. First, although we examined a broad number of geriatric syndromes, several other geriatric syndromes experienced by hospitalized older adults were not addressed including: fecal incontinence, insomnia, and functional impairment. These syndromes were intentionally excluded from the study a priori due to reasons of feasibility and scope. Second, unlike the Beer's 2015 criteria, the MAGS list does not sub‐classify associations of medications with geriatric syndromes for patients with specific diseases (eg, heart failure). In fact, our MAGS list included medications often indicated in treating these diagnoses. A clinician must work with the patient to weigh the disease‐specific benefits of some medications with the potential effect on geriatric syndrome symptoms and outcomes. Third, the instrument has a very high sensitivity, which was intended to generate an inclusive list of medications that enables providers to weigh risks of geriatric syndromes with the intended indication benefit. The objective is not to use this list as a reflexive tool but rather help clinicians identify a starting point to address geriatric syndromes in their patients to make patient‐centered medication decisions. Although the MAGS list is intentionally large (sensitive), the advent of advanced bioinformatics can enable MAGS to be assessed in the future for both clinical and research purposes. Fourth, FDA insert packages and Lexicomp databases report anything experienced by the patient while on the particular medication, but it might not necessarily imply a causative link. The high use of MAGS and the specific geriatric syndrome may coexist due to the high prevalence and interplay of multimorbidity, polypharmacy, and geriatric syndromes in this population. Last, the list was developed by expert panel members predominantly from a single institution, which may introduce bias. Despite these limitations, the prevalence of these medications in a sample of patients transitioning from acute to postacute care highlights the utility of the MAGS list in future clinical research and quality improvement endeavors.

In conclusion, the MAGS list provides a comprehensive and sensitive indicator of medications associated with any of 6 geriatric syndromes regardless of medication indication and appropriateness. The MAGS list provides an overall degree of medication burden with respect to geriatric syndromes and a foundation for future research to assess the relationship between the presence of geriatric syndromes and syndrome‐associated medications. The MAGS list is an important first step in summarizing the data that link medications to geriatric syndromes. Future studies are needed to broaden the analysis of MAGS for other common geriatric syndromes and to identify new and emerging medications not present during the time of this analysis. The MAGS list has the potential to facilitate deprescribing efforts needed to combat the epidemic of overprescribing that may be contributing to the burden of geriatric syndromes among older patients.

Geriatric syndromes are common clinical conditions in older adults that do not fall into specific disease categories. Unlike the traditional definition of a syndrome, geriatric syndrome refers to a condition that is mediated by multiple shared underlying risk factors.[1, 2] Conditions commonly referred to as geriatric syndromes include delirium, cognitive impairment, falls, unintentional weight loss, depressive symptoms, and incontinence. Even though many perceive it as medical misnomer,[3] geriatric syndromes have been shown to negatively impact quality of life and activities of daily living in older adults.[2] They are also associated with adverse outcomes such as increased healthcare utilization, functional decline, and mortality, even after adjusting for age and disease severity.[4, 5, 6] Hospitalized older adults, including those discharged to skilled nursing facilities (SNFs)[7, 8] are particularly at high risk for new‐onset or exacerbation of geriatric syndromes and poor outcomes.[7, 9, 10] However, hospital providers seldom assess, manage, or document geriatric syndromes because they are often overshadowed by disease conditions that lead to an acute episode requiring hospitalization (e.g., heart disease).[11]

Pharmacotherapy is the cornerstone of hospital treatment, and it is well‐known that it affects multiple physiologic systems causing side effects apart from the condition they are approved to treat. Given that geriatric syndromes are a result of impairments in multiple organ systems, it is plausible that pharmacotherapy may initiate or worsen these syndromes.[12] Medication‐related problems in older adults are well known. Polypharmacy and adverse drug events (as a result of drug‐drug/disease interactions and changes in pharmacokinetics and pharmacodynamics) are prevalent in multimorbid elderly patients.[13, 14, 15, 16] The prescribing cascade[17] increases the medication burden and may be a contributing factor for geriatric syndromes in hospitalized patients.[18] For instance, laxatives may be prescribed to counteract constipation caused by anticholinergic drugs.

The American Geriatric Society (AGS) Beers list[19, 20] and similar criteria[21] provide excellent resources to identify medications with potentially harmful interactions or adverse effects in older adults. Although these lists include medicines associated with a specific geriatric syndrome, they were not developed to explicitly link medicines across multiple geriatric syndromes, regardless of indication or appropriateness. For example, medications that effect important geriatric syndromes like unintentional weight/appetite loss, depression, and urinary incontinence are not extensively covered. In addition, disease‐appropriate medications (eg, ‐blockers for systolic heart failure), that may be associated with a geriatric syndrome (eg, falls) are not included; however, they may be important to consider for a patient and clinician who are weighing the disease benefits compared to the geriatric syndrome‐related risks. Finally, the AGS 2015 Beers criteria panel mentions the limitation that many medication associations may be excluded because older adults are less represented in clinical trials.[20] Clinicians are currently limited in identifying medications potentially contributing to a broad set of geriatric syndromes in their patients without a specific list of medications associated with geriatric syndromes (MAGS).[20]

In response to this gap, identifying these medications is important and should be a starting point in efforts toward prevention and treatment of geriatric syndromes. The 2 main objectives of this study were to first identify medications that may meaningfully contribute to 6 geriatric syndromes and subsequently describe the frequency of these medications in a population transitioning from acute care to postacute care to highlight the need and potential impact of such a list.

METHODS

This study included 2 phases that aligned with our 2 primary objectives. Phase 1 involved identifying medications associated with 6 geriatric syndromes, and phase 2 included a cross‐sectional analysis of the prevalence of these medications in a sample of patients discharged to SNFs.

Phase 1: Development of the MAGS List

Figure 1 depicts the underlying conceptual model and approach that was used in phase 1. The interaction between the patient factors and medication leads to polypharmacy that contributes to geriatric syndromes and additional adverse outcomes. As a starting point for mitigating geriatric syndromes, we used an iterative analytical process to identify a list of medications associated with the following geriatric syndromes that were documented to be highly prevalent in patients discharged to SNFs: cognitive impairment, delirium, falls, unintentional weight and/or appetite loss, urinary incontinence, and depression.[8] To be inclusive and sensitive, our approach differed from traditional systematic reviews, and in fact was meant to bring together much of the established systematic literature about disparate geriatric syndromes in 1 place, because patients often do not experience a geriatric syndrome in isolation, but rather experience multiple geriatric syndromes.[8] The MAGS list had 3 main inclusion criteria (Figure 1): (1) evidence in the published literature (systematic reviews, cohort studies, randomized clinical trials) that the medication is related to the syndrome, (2) expert panel opinion, and (3) drug databases (Lexicomp Online database[22] and/or US Food and Drug Administration [FDA]approved package inserts).[23] We generated an initial list of medications based on these 3 main criteria to identify medications with significant associations to each geriatric syndrome. The list was further expanded and vetted using an iterative review of each medication list as it related to each geriatric syndrome through a series of group meetings focused around each geriatric syndrome. Following further discussion, we obtained agreement among all team members for medications included in the final list. For each geriatric syndrome, we excluded medications from consideration if they were used to treat the same geriatric syndrome (eg, ‐adrenergic blockers used to treat incontinence in men were listed as associated with incontinence only in women). We classified medications according to the Established Pharmacologic Class available at the FDA website. We also compared our final MAGS list with the 2015 AGS Beer's list[20] by identifying medications that were related to the 6 geriatric syndromes. This included Beers[20]‐cited rationale of anticholinergic, extrapyramidal symptoms, orthostatic hypotension (eg, falls), high‐risk adverse central nervous system effects, sedating, cognitive decline (eg, antipsychotics), delirium, falls, fractures, incontinence, and gastrointestinal (eg, nausea, vomiting). Specifically, we assessed whether the medications were included as inappropriate by the AGS Beers 2015[20] list and also whether they documented the syndrome association for that medication.

Figure 1

RESULTS

DISCUSSION

An iterative process of evidence review by a multidisciplinary panel resulted in a list of 513 medications associated with 6 common geriatric syndromes. This analysis demonstrated that hospitalized, older patients discharged to SNFs were frequently prescribed MAGS. The rate of prescribing ranged from 100% of patients with a medication associated with falls to 36% for unintentional weight loss. Moreover, an alarming 43% of all medications at hospital discharge were MAGS. For this vulnerable population, the combination of high prevalence of MAGS and high risk of geriatric syndromes emphasize a need to critically review the risks and benefits of MAGS throughout hospitalization and at the time of discharge.

A body of evidence demonstrates that many drugs in a typical older adult regimen have no specific clinical indication, are considered inappropriate, or have uncertain efficacy in the geriatric population.[24, 25, 26] This study builds on the foundational work described in landmark reviews such as the AGS Beers[20] and STOPP/START[21] (Screening Tool of Older Persons' Potentially Inappropriate Prescriptions/Screening Tool to Alert doctors to Right, i.e. appropriate indicated Treatment) criteria. Both of these tools, however, were specifically designed as screening tools to identify medications considered unsafe for older adults under most circumstances and within specific illness states.[19, 20, 21] They are most often utilized when starting a medication to avoid acute adverse events. In contrast, the MAGS list was developed to be inclusive of medications that are often appropriate for many medical diagnoses but may also contribute to underlying geriatric syndromes that are more chronic in nature. In addition, inclusion of such medicines increases the sensitivity of screening for medications that can be targeted through patient‐centered deprescribing efforts when clinically appropriate.

A major strength of this study is that we bring together evidence across a spectrum of geriatric syndromes commonly experienced by hospitalized elders. In addition to evaluating multiple syndromes, we applied multiple modalities; particularly the use of an iterative review process by a multidisciplinary team of experts and using Lexicomp and FDA insert packages for linking medications to specific geriatric conditions. The inclusion criteria were broadened beyond single sources of evidence in an effort to capture a comprehensive list of medications. As a result, the MAGS list can be implemented as a screening tool for deprescribing interventions and assessing medication appropriateness to address individual or clusters of geriatric syndromes within a patient.

In addition to expanding this knowledge base, clinical relevance of the MAGS list is highlighted by its application to a sample of hospitalized older adults discharged to SNFs, a cohort known to experience geriatric syndromes. In fact, 43% of patients' medications at hospital discharge were MAGS. Importantly, due to the cross‐sectional nature of this study, we cannot be certain if the medication caused or potentiated each of the geriatric syndromes. However, hospitals and SNFs are devoting major resources toward reduction of falls, avoidance of urinary catheter use, and reduction of preventable readmissions. These efforts can be complemented by considering the number of medications associated with falls, urinary incontinence, and overall MAGS burden. The striking prevalence of MAGS demonstrates a rigorous need to weigh the risks and benefits of these medications. Above all, the intent of this study is not to propose that any MAGS be reflexively stopped, but rather that the MAGS list should facilitate a holistic approach to care for the complex older adult. For example, standard therapies such as gabapentin may be appropriate for treating neuralgic pain but may also contribute to falls and urinary incontinence. Thus, alternative pain treatments could be selected in place of gabapentin for a 75‐year old patient who is experiencing recurrent falls and increasing incontinence. Therefore, the MAGS list enables a patient‐provider discussion wherein medications' therapeutic benefits can be weighed against risks posed by specific clusters of geriatric syndromes, potential impact on quality of life, and consistency with goals of care.

This study has some limitations. First, although we examined a broad number of geriatric syndromes, several other geriatric syndromes experienced by hospitalized older adults were not addressed including: fecal incontinence, insomnia, and functional impairment. These syndromes were intentionally excluded from the study a priori due to reasons of feasibility and scope. Second, unlike the Beer's 2015 criteria, the MAGS list does not sub‐classify associations of medications with geriatric syndromes for patients with specific diseases (eg, heart failure). In fact, our MAGS list included medications often indicated in treating these diagnoses. A clinician must work with the patient to weigh the disease‐specific benefits of some medications with the potential effect on geriatric syndrome symptoms and outcomes. Third, the instrument has a very high sensitivity, which was intended to generate an inclusive list of medications that enables providers to weigh risks of geriatric syndromes with the intended indication benefit. The objective is not to use this list as a reflexive tool but rather help clinicians identify a starting point to address geriatric syndromes in their patients to make patient‐centered medication decisions. Although the MAGS list is intentionally large (sensitive), the advent of advanced bioinformatics can enable MAGS to be assessed in the future for both clinical and research purposes. Fourth, FDA insert packages and Lexicomp databases report anything experienced by the patient while on the particular medication, but it might not necessarily imply a causative link. The high use of MAGS and the specific geriatric syndrome may coexist due to the high prevalence and interplay of multimorbidity, polypharmacy, and geriatric syndromes in this population. Last, the list was developed by expert panel members predominantly from a single institution, which may introduce bias. Despite these limitations, the prevalence of these medications in a sample of patients transitioning from acute to postacute care highlights the utility of the MAGS list in future clinical research and quality improvement endeavors.

In conclusion, the MAGS list provides a comprehensive and sensitive indicator of medications associated with any of 6 geriatric syndromes regardless of medication indication and appropriateness. The MAGS list provides an overall degree of medication burden with respect to geriatric syndromes and a foundation for future research to assess the relationship between the presence of geriatric syndromes and syndrome‐associated medications. The MAGS list is an important first step in summarizing the data that link medications to geriatric syndromes. Future studies are needed to broaden the analysis of MAGS for other common geriatric syndromes and to identify new and emerging medications not present during the time of this analysis. The MAGS list has the potential to facilitate deprescribing efforts needed to combat the epidemic of overprescribing that may be contributing to the burden of geriatric syndromes among older patients.

Few today deny the importance of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.[1, 2] The Centers for Medicare and Medicaid Services' (CMS) Value Based Purchasing incentive, sympathy for the ill, and relationships between the patient experience and quality of care provide sufficient justification.[3, 4] How to improve the experience scores is not well understood. The national scores have improved only modestly over the past 3 years.[5, 6]

Clinicians may not typically compartmentalize what they do to improve outcomes versus the patient experience. A possible source for new improvement strategies is to understand the types of patients in which both adverse outcomes and suboptimal experiences are likely to occur, then redesign the multidisciplinary care processes to address both concurrently.[7] Previous studies support the existence of a relationship between a higher mortality risk on admission and subsequent worse outcomes, as well as a relationship between worse outcomes and lower HCAHPS scores.[8, 9, 10, 11, 12, 13] We hypothesized the mortality risk on admission, patient experience, and outcomes might share a triad relationship (Figure 1). In this article we explore the third edge of this triangle, the association between the mortality risk on admission and the subsequent patient experience.

Figure 1

METHODS

We studied HCAHPS from 5 midwestern US hospitals having 113, 136, 304, 443, and 537 licensed beds, affiliated with the same multistate healthcare system. HCAHPS telephone surveys were administered via a vendor to a random sample of inpatients 18 years of age or older discharged from January 1, 2012 through June 30, 2014. Per CMS guidelines, surveyed patients must have been discharged alive after a hospital stay of at least 1 night.[14] Patients ineligible to be surveyed included those discharged to skilled nursing facilities or hospice care.[14] Because not all study hospitals provided obstetrical services, we restricted the analyses to medical and surgical respondents. With the permission of the local institutional review board, subjects' survey responses were linked confidentially to their clinical data.

We focused on the 8 dimensions of the care experience used in the CMS Value Based Purchasing program: communication with doctors, communication with nurses, responsiveness of hospital staff, pain management, communication about medicines, discharge information, hospital environment, and an overall rating of the hospital.[2] Following the scoring convention for publicly reported results, we dichotomized the 4‐level Likert scales into the most favorable response possible (always) versus all other responses.[15] Similarly we dichotomized the hospital rating scale at 9 and above for the most favorable response.

Our unit of analysis was an individual hospitalization. Our primary outcome of interest was whether or not the respondent provided the most favorable response for all questions answered within a given domain. For example, for the physician communication domain, the patient must have answered always to each of the 3 questions answered within the domain. This approach is appropriate for learning which patient‐level factors influence the survey responses, but differs from that used for the publically reported domain scores for which the relative performance of hospitals is the focus.[16] For the latter, the hospital was the unit of analysis, and the domain score was basically the average of the percentages of top box scores for the questions within a domain. For example, if 90% respondents from a hospital provided a top box response for courtesy, 80% for listening, and 70% for explanation, the hospital's physician communication score would be (90 + 80 + 70)/3 = 80%.[17]

Our primary explanatory variable was a binary high versus low mortality‐risk status of the respondent on admission based on age, gender, prior hospitalizations, clinical laboratory values, and diagnoses present on admission.[12] The calculated mortality risk was then dichotomized prior to the analysis at a probability of dying equal to 0.07 or higher. This corresponded roughly to the top quintile of predicted risk found in prior studies.[12, 13] During the study period, only 2 of the hospitals had the capability of generating mortality scores in real time, so for this study the mortality risk was calculated retrospectively, using information deemed present on admission.[12]

To estimate the sample size, we assumed that the high‐risk strata contained approximately 13% of respondents, and that the true percent of top box responses from patients in the lower‐risk stratum was approximately 80% for each domain. A meaningful difference in the proportion of most favorable responses was considered as an odds ratio (OR) of 0.75 for high risk versus low risk. A significance level of P < 0.003 was set to control study‐wide type I error due to multiple comparisons. We determined that for each dimension, approximately 8583 survey responses would be required for low‐risk patients and approximately 1116 responses for high‐risk patients to achieve 80% power under these assumptions. We were able to accrue the target number of surveys for all but 3 domains (pain management, communication about medicines, and hospital environment) because of data availability, and because patients are allowed to skip questions that do not apply. Univariate relationships were examined with ², t test, and Fisher exact tests where indicated. Generalized linear mixed regression models with a logit link were fit to determine the association between patient mortality risk and the top box experience for each of the HCAHPS domains and for the overall rating. The patient's hospital was considered a random intercept to account for the patient‐hospital hierarchy and the unmeasured hospital‐specific practices. The multivariable models controlled for gender plus the HCAHPS patient‐mix adjustment variables of age, education, self‐rated health, language spoken at home, service line, and the number of days elapsed between the date of discharge and date of the survey.[18, 19, 20, 21] In keeping with the industry analyses, a second order interaction variable was included between surgery patients and age.[19] We considered the potential collinearity between the mortality risk status, age, and patient self‐reported health. We found the variance inflation factors were small, so we drew inference from the full multivariable model.

We also performed a post hoc sensitivity analysis to determine if our conclusions were biased due to missing patient responses for the risk‐adjustment variables. Accordingly, we imputed the response level most negatively associated with most HCAHPS domains as previously reported and reran the multivariable models.[19] We did not find a meaningful change in our conclusions (see Supporting Figure 1 in the online version of this article).

RESULTS

The hospitals discharged 152,333 patients during the study period, 39,905 of whom (26.2 %) had a predicted 30‐day mortality risk greater or equal to 0.07 (Table 1). Of the 36,280 high‐risk patients discharged alive, 5901 (16.3%) died in the ensuing 30 days, and 7951 (22%) were readmitted.

The high‐risk subset was under‐represented in those who completed the HCAHPS survey with 7% (2513/36,280) completing surveys compared to 13% of low‐risk patients (14,996/111,600) (P < 0.0001). Moreover, compared to high‐risk patients who were alive at discharge but did not complete surveys, high‐risk survey respondents were less likely to die within 30 days (16/2513 = 0.64% vs 5885/33,767 = 17.4%, P < 0.0001), and less likely to be readmitted (424/2513 = 16.9% vs 7527/33,767 = 22.3%, P < 0.0001).

On average, high‐risk respondents (compared to low risk) were slightly less likely to be female (52.4% vs 57.9%), less educated (30.6% with some college vs 42.3%), less likely to have been on a surgical service (13.8% vs 42.5%), and less likely to report good or better health (49.0% vs 68.0%, all P < 0.0001). High‐risk respondents were also older (76.6 vs 63.1 years), stayed in the hospital longer (4.6 vs 3.5 days), and received care from more specialties (2.2 vs 1.7 specialties) (all P < 0.0001). High‐risk respondents experienced more 30‐day readmissions (16.9% vs 8.1%) and deaths within 30 days (0.6 % vs 0.1 %, all P < 0.0001) than their low‐risk counterparts.

High‐risk respondents were less likely to provide the most favorable response (unadjusted) for all HCAHPS domains compared to low‐risk respondents, although the difference was not significant for discharge information (Table 1, Figure 2A). The gradient between high‐risk and low‐risk patients was seen for all domains within each hospital except for pain management, hospital environment, and overall rating (Figure 3).

Figure 2

Figure 3

The multivariable regression models examined whether the mortality risk on admission simply represented older medical patients and/or those who considered themselves unhealthy (Figure 2B) (see Supporting Table 1 in the online version of this article). Accounting for hospital, age, gender, language, self‐reported health, educational level, service line, and days elapsed from discharge, respondents in the high‐mortality‐risk stratum were still less likely to report an always experience for doctor communication (OR: 0.85; 95% confidence interval [CI]: 0.77‐0.94) and responsiveness of hospital staff (OR: 0.77; 95% CI: 0.69‐0.85). Higher‐risk patients also tended to have less favorable experiences with nursing communication, although the CI crossed 1 (OR: 0.91; 95% CI: 0.82‐1.01). In contrast, higher‐risk patients were more likely to provide top box responses for having received discharge information (OR: 1.30; 95% CI: 1.14‐1.48). We did not find independent associations between mortality risk and the other domains when the patient risk‐adjustment factors were considered.[18, 19, 20, 21]

DISCUSSION

The high‐mortality‐risk stratum on admission contained a subset of patients who provided less favorable responses for almost all incentivized HCAHPS domains when other risk‐adjustment variables were not taken into consideration (Figure 2A). These univariate relationships weakened when we controlled for gender, the standard HCAHPS risk‐adjustment variables, and individual hospital influences (Figure 2B).[18, 19, 20, 21] After multivariable adjustment, survey respondents in the high‐risk category remained less likely to report their physicians always communicated well and to experience hospital staff responding quickly, but were more likely to report receiving discharge information. We did not find an independent association between the underlying mortality risk and the other incentivized HCAHPS domains after risk adjustment.

We are cautious with initial interpretations of our findings in light of the relatively small number of hospitals studied and the substantial survey response bias of healthier patients. Undoubtedly, the CMS exclusions of patients discharged to hospice or skilled nursing facilities provide a partial explanation for the selection bias, but the experience of those at high risk who did not complete surveys remains conjecture at this point.[14] Previous evidence suggests sicker patients and those with worse experiences are less likely to respond to the HCAHPS survey.[18, 22] On the other hand, it is possible that high‐risk nonrespondents who died could have received better communication and staff responsiveness.[23, 24] We were unable to find a previous, patient‐level study that explicitly tested the association between the admission mortality risk and the subsequent patient experience, yet our findings are consistent with a previous single‐site study of a surgical population showing lower overall ratings from patients with higher Injury Severity Scores.[25]

Our findings provide evidence of complex relationships among admission mortality risk, the 3 domains of the patient experience, and adverse outcomes, at least within the study hospitals (Figure 1). The developing field of palliative care has found very ill patients have special communication needs regarding goals of care, as well as physical symptoms, anxiety, and depression that might prompt more calls for help.[26] If these needs were more important for high‐risk compared to low‐risk patients, and were either not recognized or adequately addressed by the clinical teams at the study hospitals, then the high‐risk patients may have been less likely to perceive their physicians listened and explained things well, or that staff responded promptly to their requests for help.[27] On the other hand, the higher ratings for discharge information suggest the needs of the high‐risk patients were relatively easier to address by current practices at these hospitals. The lack of association between the mortality risk and the other HCAHPS domains may reflect the relatively stronger influence of age, gender, educational level, provider variability, and other unmeasured influences within the study sites, or that the level of patient need was similar among high‐risk and low‐risk patients within these domains.[27]

There are several possible confounders of our observed relationship between mortality risk and HCAHPS scores. The first category of confounders represents patient level variables that might impact the communication scores, some of which are part of the formula of our mortality prediction rule, for example, cognitive impairment and emergent admission.[18, 22, 27] The effect of the mortality risk could also be confounded by unmeasured patient‐level factors such as lower socioeconomic status.[28] A second category of confounders pertains to clinical outcomes and processes of care associated with serious illness irrespective of the risk of dying. More physicians involved in the care of the seriously ill (Table 1) may impact the communication scores, due to the larger opportunity for conflicting or confusing information presented to patients and their families.[29] The longer hospital stays, readmissions, and adverse events of the seriously ill may also underlie the apparent association between mortality risk and HCAHPS scores.[8, 9, 10]

Even if we do not understand precisely if and how the mortality risk might be associated with suboptimal physician communication and staff responsiveness, there may still be some value in considering how these possible relationships could be leveraged to improve patient care. We recall Berwick's insight that every system is perfectly designed to achieve the results it achieves.[7] We have previously argued for the use of mortality‐risk strata to initiate concurrent, multidisciplinary care processes to reduce adverse outcomes.[12, 13] Others have used risk‐based approaches for anticipating clinical deterioration of surgical patients, and determining the intensity of individualized case management services.[30, 31] In this framework, all patients receive a standard set of care processes, but higher‐risk patients receive additional efforts to promote better outcomes. An efficient extension of this approach is to assume patients at risk for adverse outcomes also have additional needs for communication, coordination of specialty care, and timely response to the call button. The admission mortality risk could be used as a determinant for the level of nurse staffing to reduce deaths plus shorten response time to the call button.[32, 33] Hospitalists and specialists could work together on a standard way to conference among themselves for high‐risk patients above that needed for less‐complex cases. Patients in the high‐risk strata could be screened early to see if they might benefit from the involvement of the palliative care team.[26]

Our study has limitations in addition to those already noted. First, our use of the top box as the formulation of the outcome of interest could be challenged. We chose this to be relevant to the Value‐Based Purchasing environment, but other formulations or use of other survey instruments may be needed to tease out the complex relationships we hypothesize. Next, we do not know the extent to which the patients and care processes reflected in our study represent other settings. The literature indicates some hospitals are more effective in providing care for certain subgroups of patients than for others, and that there is substantial regional variation in care intensity that is in turn associated with the patient experience.[29, 34] The mortality‐risk experience relationship for nonstudy hospitals could be weaker or stronger than what we found. Third, many hospitals may not have the capability to generate mortality scores on admission, although more hospitals may be able to do so in the future.[35] Explicit risk strata have the benefit of providing members of the multidisciplinary team with a quick preview of the clinical needs and prognoses of patients in much the way that the term baroque alerts the audience to the genre of music. Still, clinicians in any hospital could attempt to improve outcomes and experience through the use of informal risk assessment during interdisciplinary care rounds or by simply asking the team if they would be surprised if this patient died in the next year.[30, 36] Finally, we do not know if awareness of an experience risk will identify remediable practices that actually improve the experience. Clearly, future studies are needed to answer all of these concerns.

We have provided evidence that a group of patients who were at elevated risk for dying at the time of admission were more likely to have issues with physician communication and staff responsiveness than their lower‐risk counterparts. While we await future studies to confirm these findings, clinical teams can consider whether or not their patients' HCAHPS scores reflect how their system of care addresses the needs of these vulnerable people.

Few today deny the importance of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.[1, 2] The Centers for Medicare and Medicaid Services' (CMS) Value Based Purchasing incentive, sympathy for the ill, and relationships between the patient experience and quality of care provide sufficient justification.[3, 4] How to improve the experience scores is not well understood. The national scores have improved only modestly over the past 3 years.[5, 6]

Clinicians may not typically compartmentalize what they do to improve outcomes versus the patient experience. A possible source for new improvement strategies is to understand the types of patients in which both adverse outcomes and suboptimal experiences are likely to occur, then redesign the multidisciplinary care processes to address both concurrently.[7] Previous studies support the existence of a relationship between a higher mortality risk on admission and subsequent worse outcomes, as well as a relationship between worse outcomes and lower HCAHPS scores.[8, 9, 10, 11, 12, 13] We hypothesized the mortality risk on admission, patient experience, and outcomes might share a triad relationship (Figure 1). In this article we explore the third edge of this triangle, the association between the mortality risk on admission and the subsequent patient experience.

Figure 1

METHODS

We studied HCAHPS from 5 midwestern US hospitals having 113, 136, 304, 443, and 537 licensed beds, affiliated with the same multistate healthcare system. HCAHPS telephone surveys were administered via a vendor to a random sample of inpatients 18 years of age or older discharged from January 1, 2012 through June 30, 2014. Per CMS guidelines, surveyed patients must have been discharged alive after a hospital stay of at least 1 night.[14] Patients ineligible to be surveyed included those discharged to skilled nursing facilities or hospice care.[14] Because not all study hospitals provided obstetrical services, we restricted the analyses to medical and surgical respondents. With the permission of the local institutional review board, subjects' survey responses were linked confidentially to their clinical data.

We focused on the 8 dimensions of the care experience used in the CMS Value Based Purchasing program: communication with doctors, communication with nurses, responsiveness of hospital staff, pain management, communication about medicines, discharge information, hospital environment, and an overall rating of the hospital.[2] Following the scoring convention for publicly reported results, we dichotomized the 4‐level Likert scales into the most favorable response possible (always) versus all other responses.[15] Similarly we dichotomized the hospital rating scale at 9 and above for the most favorable response.

Our unit of analysis was an individual hospitalization. Our primary outcome of interest was whether or not the respondent provided the most favorable response for all questions answered within a given domain. For example, for the physician communication domain, the patient must have answered always to each of the 3 questions answered within the domain. This approach is appropriate for learning which patient‐level factors influence the survey responses, but differs from that used for the publically reported domain scores for which the relative performance of hospitals is the focus.[16] For the latter, the hospital was the unit of analysis, and the domain score was basically the average of the percentages of top box scores for the questions within a domain. For example, if 90% respondents from a hospital provided a top box response for courtesy, 80% for listening, and 70% for explanation, the hospital's physician communication score would be (90 + 80 + 70)/3 = 80%.[17]

Our primary explanatory variable was a binary high versus low mortality‐risk status of the respondent on admission based on age, gender, prior hospitalizations, clinical laboratory values, and diagnoses present on admission.[12] The calculated mortality risk was then dichotomized prior to the analysis at a probability of dying equal to 0.07 or higher. This corresponded roughly to the top quintile of predicted risk found in prior studies.[12, 13] During the study period, only 2 of the hospitals had the capability of generating mortality scores in real time, so for this study the mortality risk was calculated retrospectively, using information deemed present on admission.[12]

To estimate the sample size, we assumed that the high‐risk strata contained approximately 13% of respondents, and that the true percent of top box responses from patients in the lower‐risk stratum was approximately 80% for each domain. A meaningful difference in the proportion of most favorable responses was considered as an odds ratio (OR) of 0.75 for high risk versus low risk. A significance level of P < 0.003 was set to control study‐wide type I error due to multiple comparisons. We determined that for each dimension, approximately 8583 survey responses would be required for low‐risk patients and approximately 1116 responses for high‐risk patients to achieve 80% power under these assumptions. We were able to accrue the target number of surveys for all but 3 domains (pain management, communication about medicines, and hospital environment) because of data availability, and because patients are allowed to skip questions that do not apply. Univariate relationships were examined with ², t test, and Fisher exact tests where indicated. Generalized linear mixed regression models with a logit link were fit to determine the association between patient mortality risk and the top box experience for each of the HCAHPS domains and for the overall rating. The patient's hospital was considered a random intercept to account for the patient‐hospital hierarchy and the unmeasured hospital‐specific practices. The multivariable models controlled for gender plus the HCAHPS patient‐mix adjustment variables of age, education, self‐rated health, language spoken at home, service line, and the number of days elapsed between the date of discharge and date of the survey.[18, 19, 20, 21] In keeping with the industry analyses, a second order interaction variable was included between surgery patients and age.[19] We considered the potential collinearity between the mortality risk status, age, and patient self‐reported health. We found the variance inflation factors were small, so we drew inference from the full multivariable model.

We also performed a post hoc sensitivity analysis to determine if our conclusions were biased due to missing patient responses for the risk‐adjustment variables. Accordingly, we imputed the response level most negatively associated with most HCAHPS domains as previously reported and reran the multivariable models.[19] We did not find a meaningful change in our conclusions (see Supporting Figure 1 in the online version of this article).

RESULTS

The hospitals discharged 152,333 patients during the study period, 39,905 of whom (26.2 %) had a predicted 30‐day mortality risk greater or equal to 0.07 (Table 1). Of the 36,280 high‐risk patients discharged alive, 5901 (16.3%) died in the ensuing 30 days, and 7951 (22%) were readmitted.

The high‐risk subset was under‐represented in those who completed the HCAHPS survey with 7% (2513/36,280) completing surveys compared to 13% of low‐risk patients (14,996/111,600) (P < 0.0001). Moreover, compared to high‐risk patients who were alive at discharge but did not complete surveys, high‐risk survey respondents were less likely to die within 30 days (16/2513 = 0.64% vs 5885/33,767 = 17.4%, P < 0.0001), and less likely to be readmitted (424/2513 = 16.9% vs 7527/33,767 = 22.3%, P < 0.0001).

On average, high‐risk respondents (compared to low risk) were slightly less likely to be female (52.4% vs 57.9%), less educated (30.6% with some college vs 42.3%), less likely to have been on a surgical service (13.8% vs 42.5%), and less likely to report good or better health (49.0% vs 68.0%, all P < 0.0001). High‐risk respondents were also older (76.6 vs 63.1 years), stayed in the hospital longer (4.6 vs 3.5 days), and received care from more specialties (2.2 vs 1.7 specialties) (all P < 0.0001). High‐risk respondents experienced more 30‐day readmissions (16.9% vs 8.1%) and deaths within 30 days (0.6 % vs 0.1 %, all P < 0.0001) than their low‐risk counterparts.

High‐risk respondents were less likely to provide the most favorable response (unadjusted) for all HCAHPS domains compared to low‐risk respondents, although the difference was not significant for discharge information (Table 1, Figure 2A). The gradient between high‐risk and low‐risk patients was seen for all domains within each hospital except for pain management, hospital environment, and overall rating (Figure 3).

Figure 2

Figure 3

The multivariable regression models examined whether the mortality risk on admission simply represented older medical patients and/or those who considered themselves unhealthy (Figure 2B) (see Supporting Table 1 in the online version of this article). Accounting for hospital, age, gender, language, self‐reported health, educational level, service line, and days elapsed from discharge, respondents in the high‐mortality‐risk stratum were still less likely to report an always experience for doctor communication (OR: 0.85; 95% confidence interval [CI]: 0.77‐0.94) and responsiveness of hospital staff (OR: 0.77; 95% CI: 0.69‐0.85). Higher‐risk patients also tended to have less favorable experiences with nursing communication, although the CI crossed 1 (OR: 0.91; 95% CI: 0.82‐1.01). In contrast, higher‐risk patients were more likely to provide top box responses for having received discharge information (OR: 1.30; 95% CI: 1.14‐1.48). We did not find independent associations between mortality risk and the other domains when the patient risk‐adjustment factors were considered.[18, 19, 20, 21]

DISCUSSION

The high‐mortality‐risk stratum on admission contained a subset of patients who provided less favorable responses for almost all incentivized HCAHPS domains when other risk‐adjustment variables were not taken into consideration (Figure 2A). These univariate relationships weakened when we controlled for gender, the standard HCAHPS risk‐adjustment variables, and individual hospital influences (Figure 2B).[18, 19, 20, 21] After multivariable adjustment, survey respondents in the high‐risk category remained less likely to report their physicians always communicated well and to experience hospital staff responding quickly, but were more likely to report receiving discharge information. We did not find an independent association between the underlying mortality risk and the other incentivized HCAHPS domains after risk adjustment.

We are cautious with initial interpretations of our findings in light of the relatively small number of hospitals studied and the substantial survey response bias of healthier patients. Undoubtedly, the CMS exclusions of patients discharged to hospice or skilled nursing facilities provide a partial explanation for the selection bias, but the experience of those at high risk who did not complete surveys remains conjecture at this point.[14] Previous evidence suggests sicker patients and those with worse experiences are less likely to respond to the HCAHPS survey.[18, 22] On the other hand, it is possible that high‐risk nonrespondents who died could have received better communication and staff responsiveness.[23, 24] We were unable to find a previous, patient‐level study that explicitly tested the association between the admission mortality risk and the subsequent patient experience, yet our findings are consistent with a previous single‐site study of a surgical population showing lower overall ratings from patients with higher Injury Severity Scores.[25]

Our findings provide evidence of complex relationships among admission mortality risk, the 3 domains of the patient experience, and adverse outcomes, at least within the study hospitals (Figure 1). The developing field of palliative care has found very ill patients have special communication needs regarding goals of care, as well as physical symptoms, anxiety, and depression that might prompt more calls for help.[26] If these needs were more important for high‐risk compared to low‐risk patients, and were either not recognized or adequately addressed by the clinical teams at the study hospitals, then the high‐risk patients may have been less likely to perceive their physicians listened and explained things well, or that staff responded promptly to their requests for help.[27] On the other hand, the higher ratings for discharge information suggest the needs of the high‐risk patients were relatively easier to address by current practices at these hospitals. The lack of association between the mortality risk and the other HCAHPS domains may reflect the relatively stronger influence of age, gender, educational level, provider variability, and other unmeasured influences within the study sites, or that the level of patient need was similar among high‐risk and low‐risk patients within these domains.[27]

There are several possible confounders of our observed relationship between mortality risk and HCAHPS scores. The first category of confounders represents patient level variables that might impact the communication scores, some of which are part of the formula of our mortality prediction rule, for example, cognitive impairment and emergent admission.[18, 22, 27] The effect of the mortality risk could also be confounded by unmeasured patient‐level factors such as lower socioeconomic status.[28] A second category of confounders pertains to clinical outcomes and processes of care associated with serious illness irrespective of the risk of dying. More physicians involved in the care of the seriously ill (Table 1) may impact the communication scores, due to the larger opportunity for conflicting or confusing information presented to patients and their families.[29] The longer hospital stays, readmissions, and adverse events of the seriously ill may also underlie the apparent association between mortality risk and HCAHPS scores.[8, 9, 10]

Even if we do not understand precisely if and how the mortality risk might be associated with suboptimal physician communication and staff responsiveness, there may still be some value in considering how these possible relationships could be leveraged to improve patient care. We recall Berwick's insight that every system is perfectly designed to achieve the results it achieves.[7] We have previously argued for the use of mortality‐risk strata to initiate concurrent, multidisciplinary care processes to reduce adverse outcomes.[12, 13] Others have used risk‐based approaches for anticipating clinical deterioration of surgical patients, and determining the intensity of individualized case management services.[30, 31] In this framework, all patients receive a standard set of care processes, but higher‐risk patients receive additional efforts to promote better outcomes. An efficient extension of this approach is to assume patients at risk for adverse outcomes also have additional needs for communication, coordination of specialty care, and timely response to the call button. The admission mortality risk could be used as a determinant for the level of nurse staffing to reduce deaths plus shorten response time to the call button.[32, 33] Hospitalists and specialists could work together on a standard way to conference among themselves for high‐risk patients above that needed for less‐complex cases. Patients in the high‐risk strata could be screened early to see if they might benefit from the involvement of the palliative care team.[26]

Our study has limitations in addition to those already noted. First, our use of the top box as the formulation of the outcome of interest could be challenged. We chose this to be relevant to the Value‐Based Purchasing environment, but other formulations or use of other survey instruments may be needed to tease out the complex relationships we hypothesize. Next, we do not know the extent to which the patients and care processes reflected in our study represent other settings. The literature indicates some hospitals are more effective in providing care for certain subgroups of patients than for others, and that there is substantial regional variation in care intensity that is in turn associated with the patient experience.[29, 34] The mortality‐risk experience relationship for nonstudy hospitals could be weaker or stronger than what we found. Third, many hospitals may not have the capability to generate mortality scores on admission, although more hospitals may be able to do so in the future.[35] Explicit risk strata have the benefit of providing members of the multidisciplinary team with a quick preview of the clinical needs and prognoses of patients in much the way that the term baroque alerts the audience to the genre of music. Still, clinicians in any hospital could attempt to improve outcomes and experience through the use of informal risk assessment during interdisciplinary care rounds or by simply asking the team if they would be surprised if this patient died in the next year.[30, 36] Finally, we do not know if awareness of an experience risk will identify remediable practices that actually improve the experience. Clearly, future studies are needed to answer all of these concerns.

We have provided evidence that a group of patients who were at elevated risk for dying at the time of admission were more likely to have issues with physician communication and staff responsiveness than their lower‐risk counterparts. While we await future studies to confirm these findings, clinical teams can consider whether or not their patients' HCAHPS scores reflect how their system of care addresses the needs of these vulnerable people.

Few today deny the importance of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.[1, 2] The Centers for Medicare and Medicaid Services' (CMS) Value Based Purchasing incentive, sympathy for the ill, and relationships between the patient experience and quality of care provide sufficient justification.[3, 4] How to improve the experience scores is not well understood. The national scores have improved only modestly over the past 3 years.[5, 6]

Clinicians may not typically compartmentalize what they do to improve outcomes versus the patient experience. A possible source for new improvement strategies is to understand the types of patients in which both adverse outcomes and suboptimal experiences are likely to occur, then redesign the multidisciplinary care processes to address both concurrently.[7] Previous studies support the existence of a relationship between a higher mortality risk on admission and subsequent worse outcomes, as well as a relationship between worse outcomes and lower HCAHPS scores.[8, 9, 10, 11, 12, 13] We hypothesized the mortality risk on admission, patient experience, and outcomes might share a triad relationship (Figure 1). In this article we explore the third edge of this triangle, the association between the mortality risk on admission and the subsequent patient experience.

Figure 1

METHODS

We studied HCAHPS from 5 midwestern US hospitals having 113, 136, 304, 443, and 537 licensed beds, affiliated with the same multistate healthcare system. HCAHPS telephone surveys were administered via a vendor to a random sample of inpatients 18 years of age or older discharged from January 1, 2012 through June 30, 2014. Per CMS guidelines, surveyed patients must have been discharged alive after a hospital stay of at least 1 night.[14] Patients ineligible to be surveyed included those discharged to skilled nursing facilities or hospice care.[14] Because not all study hospitals provided obstetrical services, we restricted the analyses to medical and surgical respondents. With the permission of the local institutional review board, subjects' survey responses were linked confidentially to their clinical data.

We focused on the 8 dimensions of the care experience used in the CMS Value Based Purchasing program: communication with doctors, communication with nurses, responsiveness of hospital staff, pain management, communication about medicines, discharge information, hospital environment, and an overall rating of the hospital.[2] Following the scoring convention for publicly reported results, we dichotomized the 4‐level Likert scales into the most favorable response possible (always) versus all other responses.[15] Similarly we dichotomized the hospital rating scale at 9 and above for the most favorable response.

Our unit of analysis was an individual hospitalization. Our primary outcome of interest was whether or not the respondent provided the most favorable response for all questions answered within a given domain. For example, for the physician communication domain, the patient must have answered always to each of the 3 questions answered within the domain. This approach is appropriate for learning which patient‐level factors influence the survey responses, but differs from that used for the publically reported domain scores for which the relative performance of hospitals is the focus.[16] For the latter, the hospital was the unit of analysis, and the domain score was basically the average of the percentages of top box scores for the questions within a domain. For example, if 90% respondents from a hospital provided a top box response for courtesy, 80% for listening, and 70% for explanation, the hospital's physician communication score would be (90 + 80 + 70)/3 = 80%.[17]

Our primary explanatory variable was a binary high versus low mortality‐risk status of the respondent on admission based on age, gender, prior hospitalizations, clinical laboratory values, and diagnoses present on admission.[12] The calculated mortality risk was then dichotomized prior to the analysis at a probability of dying equal to 0.07 or higher. This corresponded roughly to the top quintile of predicted risk found in prior studies.[12, 13] During the study period, only 2 of the hospitals had the capability of generating mortality scores in real time, so for this study the mortality risk was calculated retrospectively, using information deemed present on admission.[12]

To estimate the sample size, we assumed that the high‐risk strata contained approximately 13% of respondents, and that the true percent of top box responses from patients in the lower‐risk stratum was approximately 80% for each domain. A meaningful difference in the proportion of most favorable responses was considered as an odds ratio (OR) of 0.75 for high risk versus low risk. A significance level of P < 0.003 was set to control study‐wide type I error due to multiple comparisons. We determined that for each dimension, approximately 8583 survey responses would be required for low‐risk patients and approximately 1116 responses for high‐risk patients to achieve 80% power under these assumptions. We were able to accrue the target number of surveys for all but 3 domains (pain management, communication about medicines, and hospital environment) because of data availability, and because patients are allowed to skip questions that do not apply. Univariate relationships were examined with ², t test, and Fisher exact tests where indicated. Generalized linear mixed regression models with a logit link were fit to determine the association between patient mortality risk and the top box experience for each of the HCAHPS domains and for the overall rating. The patient's hospital was considered a random intercept to account for the patient‐hospital hierarchy and the unmeasured hospital‐specific practices. The multivariable models controlled for gender plus the HCAHPS patient‐mix adjustment variables of age, education, self‐rated health, language spoken at home, service line, and the number of days elapsed between the date of discharge and date of the survey.[18, 19, 20, 21] In keeping with the industry analyses, a second order interaction variable was included between surgery patients and age.[19] We considered the potential collinearity between the mortality risk status, age, and patient self‐reported health. We found the variance inflation factors were small, so we drew inference from the full multivariable model.

We also performed a post hoc sensitivity analysis to determine if our conclusions were biased due to missing patient responses for the risk‐adjustment variables. Accordingly, we imputed the response level most negatively associated with most HCAHPS domains as previously reported and reran the multivariable models.[19] We did not find a meaningful change in our conclusions (see Supporting Figure 1 in the online version of this article).

RESULTS

The hospitals discharged 152,333 patients during the study period, 39,905 of whom (26.2 %) had a predicted 30‐day mortality risk greater or equal to 0.07 (Table 1). Of the 36,280 high‐risk patients discharged alive, 5901 (16.3%) died in the ensuing 30 days, and 7951 (22%) were readmitted.

The high‐risk subset was under‐represented in those who completed the HCAHPS survey with 7% (2513/36,280) completing surveys compared to 13% of low‐risk patients (14,996/111,600) (P < 0.0001). Moreover, compared to high‐risk patients who were alive at discharge but did not complete surveys, high‐risk survey respondents were less likely to die within 30 days (16/2513 = 0.64% vs 5885/33,767 = 17.4%, P < 0.0001), and less likely to be readmitted (424/2513 = 16.9% vs 7527/33,767 = 22.3%, P < 0.0001).

On average, high‐risk respondents (compared to low risk) were slightly less likely to be female (52.4% vs 57.9%), less educated (30.6% with some college vs 42.3%), less likely to have been on a surgical service (13.8% vs 42.5%), and less likely to report good or better health (49.0% vs 68.0%, all P < 0.0001). High‐risk respondents were also older (76.6 vs 63.1 years), stayed in the hospital longer (4.6 vs 3.5 days), and received care from more specialties (2.2 vs 1.7 specialties) (all P < 0.0001). High‐risk respondents experienced more 30‐day readmissions (16.9% vs 8.1%) and deaths within 30 days (0.6 % vs 0.1 %, all P < 0.0001) than their low‐risk counterparts.

High‐risk respondents were less likely to provide the most favorable response (unadjusted) for all HCAHPS domains compared to low‐risk respondents, although the difference was not significant for discharge information (Table 1, Figure 2A). The gradient between high‐risk and low‐risk patients was seen for all domains within each hospital except for pain management, hospital environment, and overall rating (Figure 3).

Figure 2

Figure 3

The multivariable regression models examined whether the mortality risk on admission simply represented older medical patients and/or those who considered themselves unhealthy (Figure 2B) (see Supporting Table 1 in the online version of this article). Accounting for hospital, age, gender, language, self‐reported health, educational level, service line, and days elapsed from discharge, respondents in the high‐mortality‐risk stratum were still less likely to report an always experience for doctor communication (OR: 0.85; 95% confidence interval [CI]: 0.77‐0.94) and responsiveness of hospital staff (OR: 0.77; 95% CI: 0.69‐0.85). Higher‐risk patients also tended to have less favorable experiences with nursing communication, although the CI crossed 1 (OR: 0.91; 95% CI: 0.82‐1.01). In contrast, higher‐risk patients were more likely to provide top box responses for having received discharge information (OR: 1.30; 95% CI: 1.14‐1.48). We did not find independent associations between mortality risk and the other domains when the patient risk‐adjustment factors were considered.[18, 19, 20, 21]

DISCUSSION

The high‐mortality‐risk stratum on admission contained a subset of patients who provided less favorable responses for almost all incentivized HCAHPS domains when other risk‐adjustment variables were not taken into consideration (Figure 2A). These univariate relationships weakened when we controlled for gender, the standard HCAHPS risk‐adjustment variables, and individual hospital influences (Figure 2B).[18, 19, 20, 21] After multivariable adjustment, survey respondents in the high‐risk category remained less likely to report their physicians always communicated well and to experience hospital staff responding quickly, but were more likely to report receiving discharge information. We did not find an independent association between the underlying mortality risk and the other incentivized HCAHPS domains after risk adjustment.

We are cautious with initial interpretations of our findings in light of the relatively small number of hospitals studied and the substantial survey response bias of healthier patients. Undoubtedly, the CMS exclusions of patients discharged to hospice or skilled nursing facilities provide a partial explanation for the selection bias, but the experience of those at high risk who did not complete surveys remains conjecture at this point.[14] Previous evidence suggests sicker patients and those with worse experiences are less likely to respond to the HCAHPS survey.[18, 22] On the other hand, it is possible that high‐risk nonrespondents who died could have received better communication and staff responsiveness.[23, 24] We were unable to find a previous, patient‐level study that explicitly tested the association between the admission mortality risk and the subsequent patient experience, yet our findings are consistent with a previous single‐site study of a surgical population showing lower overall ratings from patients with higher Injury Severity Scores.[25]

Our findings provide evidence of complex relationships among admission mortality risk, the 3 domains of the patient experience, and adverse outcomes, at least within the study hospitals (Figure 1). The developing field of palliative care has found very ill patients have special communication needs regarding goals of care, as well as physical symptoms, anxiety, and depression that might prompt more calls for help.[26] If these needs were more important for high‐risk compared to low‐risk patients, and were either not recognized or adequately addressed by the clinical teams at the study hospitals, then the high‐risk patients may have been less likely to perceive their physicians listened and explained things well, or that staff responded promptly to their requests for help.[27] On the other hand, the higher ratings for discharge information suggest the needs of the high‐risk patients were relatively easier to address by current practices at these hospitals. The lack of association between the mortality risk and the other HCAHPS domains may reflect the relatively stronger influence of age, gender, educational level, provider variability, and other unmeasured influences within the study sites, or that the level of patient need was similar among high‐risk and low‐risk patients within these domains.[27]

There are several possible confounders of our observed relationship between mortality risk and HCAHPS scores. The first category of confounders represents patient level variables that might impact the communication scores, some of which are part of the formula of our mortality prediction rule, for example, cognitive impairment and emergent admission.[18, 22, 27] The effect of the mortality risk could also be confounded by unmeasured patient‐level factors such as lower socioeconomic status.[28] A second category of confounders pertains to clinical outcomes and processes of care associated with serious illness irrespective of the risk of dying. More physicians involved in the care of the seriously ill (Table 1) may impact the communication scores, due to the larger opportunity for conflicting or confusing information presented to patients and their families.[29] The longer hospital stays, readmissions, and adverse events of the seriously ill may also underlie the apparent association between mortality risk and HCAHPS scores.[8, 9, 10]

Even if we do not understand precisely if and how the mortality risk might be associated with suboptimal physician communication and staff responsiveness, there may still be some value in considering how these possible relationships could be leveraged to improve patient care. We recall Berwick's insight that every system is perfectly designed to achieve the results it achieves.[7] We have previously argued for the use of mortality‐risk strata to initiate concurrent, multidisciplinary care processes to reduce adverse outcomes.[12, 13] Others have used risk‐based approaches for anticipating clinical deterioration of surgical patients, and determining the intensity of individualized case management services.[30, 31] In this framework, all patients receive a standard set of care processes, but higher‐risk patients receive additional efforts to promote better outcomes. An efficient extension of this approach is to assume patients at risk for adverse outcomes also have additional needs for communication, coordination of specialty care, and timely response to the call button. The admission mortality risk could be used as a determinant for the level of nurse staffing to reduce deaths plus shorten response time to the call button.[32, 33] Hospitalists and specialists could work together on a standard way to conference among themselves for high‐risk patients above that needed for less‐complex cases. Patients in the high‐risk strata could be screened early to see if they might benefit from the involvement of the palliative care team.[26]

Our study has limitations in addition to those already noted. First, our use of the top box as the formulation of the outcome of interest could be challenged. We chose this to be relevant to the Value‐Based Purchasing environment, but other formulations or use of other survey instruments may be needed to tease out the complex relationships we hypothesize. Next, we do not know the extent to which the patients and care processes reflected in our study represent other settings. The literature indicates some hospitals are more effective in providing care for certain subgroups of patients than for others, and that there is substantial regional variation in care intensity that is in turn associated with the patient experience.[29, 34] The mortality‐risk experience relationship for nonstudy hospitals could be weaker or stronger than what we found. Third, many hospitals may not have the capability to generate mortality scores on admission, although more hospitals may be able to do so in the future.[35] Explicit risk strata have the benefit of providing members of the multidisciplinary team with a quick preview of the clinical needs and prognoses of patients in much the way that the term baroque alerts the audience to the genre of music. Still, clinicians in any hospital could attempt to improve outcomes and experience through the use of informal risk assessment during interdisciplinary care rounds or by simply asking the team if they would be surprised if this patient died in the next year.[30, 36] Finally, we do not know if awareness of an experience risk will identify remediable practices that actually improve the experience. Clearly, future studies are needed to answer all of these concerns.

We have provided evidence that a group of patients who were at elevated risk for dying at the time of admission were more likely to have issues with physician communication and staff responsiveness than their lower‐risk counterparts. While we await future studies to confirm these findings, clinical teams can consider whether or not their patients' HCAHPS scores reflect how their system of care addresses the needs of these vulnerable people.