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Clinical Alerts Predict Readmission
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
Study Location
The study was conducted on 8 general medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri (January 15, 2015December 12, 2015). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or housestaff physicians under the supervision of an attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived.
Study Overview
We retrospectively evaluated all adult patients (aged >18 years) admitted through the emergency department or transferred directly to the general medicine units from other institutions. We excluded patients who died while hospitalized. All data were derived from the hospital informatics database provided by the Center for Clinical Excellence, BJC HealthCare.
Primary End Point
Readmission for any reason (ie, all‐cause readmission) to an acute care facility in the 30 days following discharge after the index hospitalization served as the primary end point. Barnes‐Jewish Hospital serves as the main teaching institution for BJC Healthcare, a large integrated healthcare system of both inpatient and outpatient care. The system includes a total of 12 hospitals and multiple community health locations in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 12 hospitals. If a patient who receives healthcare in the system presents to a nonsystem hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage. Patients with a 30‐day readmission were compared to those without a 30‐day readmission.
Variables
We recorded information regarding demographics, median income of the zip code of residence as a marker of socioeconomic status, admission to any BJC Healthcare facility within 6 months of the index admission, and comorbidities. To represent the global burden of comorbidities in each patient, we calculated their Charlson Comorbidity Index score.[12] Severity of illness was assessed using the All Patient RefinedDiagnosis Related Groups severity of illness score.
CDA Algorithm Overview
Details regarding the CDA model development and its implementation have been previously described in detail.[4, 5, 6] In brief, we applied logistic regression techniques to develop the CDA algorithm. Manually obtained vital signs, laboratory data, and pharmacy data inputted real time into the electronic medical record (EMR) were continuously assessed. The CDA algorithm searched for the 36 input variables (Table 1) as previously described from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week.[4, 5, 6] Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retain a sliding window of all the collected data points within the last 24 hours. We then subdivide these data into a series of n equally sized buckets (eg, 6 sequential buckets of 4 hours each). To capture variations within a bucket, we compute 3 values for each bucket: the minimum, maximum, and mean data points. Each of the resulting 3 n values are input to the logistic regression equation as separate variables.
| Age |
| Alanine aminotransferase |
| Alternative medicines |
| Anion gap |
| Anti‐infectives |
| Antineoplastics |
| Aspartate aminotransferase |
| Biologicals |
| Blood pressure, diastolic |
| Blood pressure, systolic |
| Calcium, serum |
| Calcium, serum, ionized |
| Cardiovascular agents |
| Central nervous system agents |
| Charlson Comorbidity Index |
| Coagulation modifiers |
| Estimated creatinine clearance |
| Gastrointestinal agents |
| Genitourinary tract agents |
| Hormones/hormone modifiers |
| Immunologic agents |
| Magnesium, serum |
| Metabolic agents |
| Miscellaneous agents |
| Nutritional products |
| Oxygen saturation, pulse oximetry |
| Phosphate, serum |
| Potassium, serum |
| Psychotherapeutic agents |
| Pulse |
| Radiologic agents |
| Respirations |
| Respiratory agents |
| Shock Index |
| Temperature |
| Topical agents |
The algorithm was first implemented in MATLAB (MathWorks, Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. The dataset's 36 input variables were divided into buckets and minimum/mean/maximum features wherever applicable, resulting in 398 variables. The first half of the original dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point the C statistic was 0.8834, with an overall accuracy of 0.9292.[5] Patients with inputted data meeting the CDA threshold had a real‐time alert sent to the hospital rapid response team prompting a patient evaluation.
Statistical Analysis
The number of patients admitted to the 8 general medicine units of Barnes‐Jewish Hospital during the study period determined the sample size. Categorical variables were compared using 2 or Fisher exact test as appropriate. Continuous variables were compared using the Mann‐Whitney U test. All analyses were 2‐tailed, and a P value of <0.05 was assumed to represent statistical significance. We relied on logistic regression for identifying variables independently associated with 30‐day readmission. Based on univariate analysis, variables significant at P < 0.15 were entered into the model. To arrive at the most parsimonious model, we utilized a stepwise backward elimination approach. We evaluated collinearity with the variance inflation factor. We report adjusted odds ratios (ORs) and 95% confidence intervals (CIs) where appropriate. The model's goodness of fit was assessed via calculation of the Hosmer‐Lemeshow test. Receiver operating characteristic (ROC) curves were used to compare the predictive models for 30‐day readmission with or without the CDA variable. All statistical analyses were performed using SPSS (version 22.0; IBM, Armonk, NY).
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.
| Variable | 30‐Day Readmission | P Value | |
|---|---|---|---|
| Yes (n = 567) | No (n = 2,448) | ||
| |||
| Age, y | 56.1 17.0 | 57.8 17.6 | 0.046 |
| Gender | |||
| Male | 252 (44.4) | 1,188 (48.5) | 0.079 |
| Female | 315 (55.6) | 1,260 (51.5) | |
| Race | |||
| Caucasian | 277 (48.9) | 1,234 (50.4) | 0.800 |
| African American | 257 (45.3) | 1,076 (44.0) | |
| Other | 33 (5.8) | 138 (5.6) | |
| Median income, dollars | 30,149 [25,23436,453] | 29,271 [24,83037,026] | 0.903 |
| BMI | 29.4 10.0 | 29.0 9.2 | 0.393 |
| APR‐DRG Severity of Illness Score | 2.6 0.4 | 2.5 0.5 | 0.152 |
| Charlson Comorbidity Index | 6 [39] | 5 [27] | <0.001 |
| ICU transfer during admission | 93 (16.4) | 410 (16.7) | 0.842 |
| Myocardial infarction | 83 (14.6) | 256 (10.5) | 0.005 |
| Congestive heart failure | 177 (31.2) | 540 (22.1) | <0.001 |
| Peripheral vascular disease | 76 (13.4) | 214 (8.7) | 0.001 |
| Cardiovascular disease | 69 (12.2) | 224 (9.2) | 0.029 |
| Dementia | 15 (2.6) | 80 (3.3) | 0.445 |
| Chronic obstructive pulmonary disease | 220 (38.8) | 855 (34.9) | 0.083 |
| Connective tissue disease | 45 (7.9) | 118 (4.8) | 0.003 |
| Peptic ulcer disease | 26 (4.6) | 111 (4.5) | 0.958 |
| Cirrhosis | 60 (10.6) | 141 (5.8) | <0.001 |
| Diabetes mellitus without end‐organ complications | 148 (26.1) | 625 (25.5) | 0.779 |
| Diabetes mellitus with end‐organ complications | 92 (16.2) | 197 (8.0) | <0.001 |
| Paralysis | 25 (4.4) | 77 (3.1) | 0.134 |
| Renal failure | 214 (37.7) | 620 (25.3) | <0.001 |
| Underlying malignancy | 85 (15.0) | 314 (12.8) | 0.171 |
| Metastatic cancer | 64 (11.3) | 163 (6.7) | <0.001 |
| Human immunodeficiency virus | 10 (1.8) | 47 (1.9) | 0.806 |
| Minimum hemoglobin, g/dL | 9.1 [7.411.4] | 10.7 [8.712.4] | <0.001 |
| Minimum creatinine, mg/dL | 1.12 [0.792.35] | 1.03 [0.791.63] | 0.006 |
| Length of stay, d | 3.8 [1.97.8] | 3.3 [1.85.9] | <0.001 |
| ED visit in the past year | 1 [03] | 0 [01] | <0.001 |
| Clinical deterioration alert triggered | 269 (47.4) | 872 (35.6%) | <0.001 |
| Insurance | |||
| Private | 111 (19.6) | 528 (21.6) | 0.020 |
| Medicare | 299 (52.7) | 1,217 (49.7) | |
| Medicaid | 129 (22.8) | 499 (20.4) | |
| Patient pay | 28 (4.9) | 204 (8.3) | |
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).
| Variables | OR | 95% CI | P Value |
|---|---|---|---|
| |||
| Clinical deterioration alert | 1.40 | 1.261.55 | 0.001 |
| Age (1‐point increments) | 1.01 | 1.011.02 | 0.003 |
| Connective tissue disease | 1.63 | 1.341.98 | 0.012 |
| Cirrhosis | 1.25 | 1.171.33 | <0.001 |
| Diabetes mellitus with end‐organ complications | 1.23 | 1.131.33 | 0.010 |
| Chronic renal disease | 1.16 | 1.081.24 | 0.034 |
| Metastatic cancer | 1.12 | 1.081.17 | 0.002 |
| Emergency department visit in previous 6 months | 1.23 | 1.201.26 | <0.001 |

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]
Disclosure
This study was funded in part by the Barnes‐Jewish Hospital Foundation and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR or NIH.
- , , . Rapid‐response teams. N Engl J Med. 2011;365:139–146.
- , , , , , . Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5:19–25.
- , , , et al. Implementation of a real‐time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39:469–473.
- , , , et al. Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
- , , , et al. A trial of a real‐time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013;8:236–242.
- , , , et al. A randomized trial of real‐time automated clinical deterioration alerts sent to a rapid response team. J Hosp Med. 2014;9:424–429.
- , . Revisiting hospital readmissions JAMA. 2013;309:398–400.
- , , , . Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7:224–230.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011; 155:520–528.
- , , , et al. International variation in and factors associated with hospital readmission after myocardial infarction. JAMA. 2012;307:66–74.
- , , , , . Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2014;11:685–694.
- , , , , , . Assessing illness severity: does clinical judgement work? J Chronic Dis. 1986;39:439–452.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48:981–988.
- , , , et al. The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30‐day readmission. J Hosp Med. 2013;8:689–695.
- , , . The Medicare hospital readmissions reduction program: time for reform. JAMA. 2015;314:347–348.
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
Study Location
The study was conducted on 8 general medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri (January 15, 2015December 12, 2015). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or housestaff physicians under the supervision of an attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived.
Study Overview
We retrospectively evaluated all adult patients (aged >18 years) admitted through the emergency department or transferred directly to the general medicine units from other institutions. We excluded patients who died while hospitalized. All data were derived from the hospital informatics database provided by the Center for Clinical Excellence, BJC HealthCare.
Primary End Point
Readmission for any reason (ie, all‐cause readmission) to an acute care facility in the 30 days following discharge after the index hospitalization served as the primary end point. Barnes‐Jewish Hospital serves as the main teaching institution for BJC Healthcare, a large integrated healthcare system of both inpatient and outpatient care. The system includes a total of 12 hospitals and multiple community health locations in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 12 hospitals. If a patient who receives healthcare in the system presents to a nonsystem hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage. Patients with a 30‐day readmission were compared to those without a 30‐day readmission.
Variables
We recorded information regarding demographics, median income of the zip code of residence as a marker of socioeconomic status, admission to any BJC Healthcare facility within 6 months of the index admission, and comorbidities. To represent the global burden of comorbidities in each patient, we calculated their Charlson Comorbidity Index score.[12] Severity of illness was assessed using the All Patient RefinedDiagnosis Related Groups severity of illness score.
CDA Algorithm Overview
Details regarding the CDA model development and its implementation have been previously described in detail.[4, 5, 6] In brief, we applied logistic regression techniques to develop the CDA algorithm. Manually obtained vital signs, laboratory data, and pharmacy data inputted real time into the electronic medical record (EMR) were continuously assessed. The CDA algorithm searched for the 36 input variables (Table 1) as previously described from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week.[4, 5, 6] Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retain a sliding window of all the collected data points within the last 24 hours. We then subdivide these data into a series of n equally sized buckets (eg, 6 sequential buckets of 4 hours each). To capture variations within a bucket, we compute 3 values for each bucket: the minimum, maximum, and mean data points. Each of the resulting 3 n values are input to the logistic regression equation as separate variables.
| Age |
| Alanine aminotransferase |
| Alternative medicines |
| Anion gap |
| Anti‐infectives |
| Antineoplastics |
| Aspartate aminotransferase |
| Biologicals |
| Blood pressure, diastolic |
| Blood pressure, systolic |
| Calcium, serum |
| Calcium, serum, ionized |
| Cardiovascular agents |
| Central nervous system agents |
| Charlson Comorbidity Index |
| Coagulation modifiers |
| Estimated creatinine clearance |
| Gastrointestinal agents |
| Genitourinary tract agents |
| Hormones/hormone modifiers |
| Immunologic agents |
| Magnesium, serum |
| Metabolic agents |
| Miscellaneous agents |
| Nutritional products |
| Oxygen saturation, pulse oximetry |
| Phosphate, serum |
| Potassium, serum |
| Psychotherapeutic agents |
| Pulse |
| Radiologic agents |
| Respirations |
| Respiratory agents |
| Shock Index |
| Temperature |
| Topical agents |
The algorithm was first implemented in MATLAB (MathWorks, Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. The dataset's 36 input variables were divided into buckets and minimum/mean/maximum features wherever applicable, resulting in 398 variables. The first half of the original dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point the C statistic was 0.8834, with an overall accuracy of 0.9292.[5] Patients with inputted data meeting the CDA threshold had a real‐time alert sent to the hospital rapid response team prompting a patient evaluation.
Statistical Analysis
The number of patients admitted to the 8 general medicine units of Barnes‐Jewish Hospital during the study period determined the sample size. Categorical variables were compared using 2 or Fisher exact test as appropriate. Continuous variables were compared using the Mann‐Whitney U test. All analyses were 2‐tailed, and a P value of <0.05 was assumed to represent statistical significance. We relied on logistic regression for identifying variables independently associated with 30‐day readmission. Based on univariate analysis, variables significant at P < 0.15 were entered into the model. To arrive at the most parsimonious model, we utilized a stepwise backward elimination approach. We evaluated collinearity with the variance inflation factor. We report adjusted odds ratios (ORs) and 95% confidence intervals (CIs) where appropriate. The model's goodness of fit was assessed via calculation of the Hosmer‐Lemeshow test. Receiver operating characteristic (ROC) curves were used to compare the predictive models for 30‐day readmission with or without the CDA variable. All statistical analyses were performed using SPSS (version 22.0; IBM, Armonk, NY).
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.
| Variable | 30‐Day Readmission | P Value | |
|---|---|---|---|
| Yes (n = 567) | No (n = 2,448) | ||
| |||
| Age, y | 56.1 17.0 | 57.8 17.6 | 0.046 |
| Gender | |||
| Male | 252 (44.4) | 1,188 (48.5) | 0.079 |
| Female | 315 (55.6) | 1,260 (51.5) | |
| Race | |||
| Caucasian | 277 (48.9) | 1,234 (50.4) | 0.800 |
| African American | 257 (45.3) | 1,076 (44.0) | |
| Other | 33 (5.8) | 138 (5.6) | |
| Median income, dollars | 30,149 [25,23436,453] | 29,271 [24,83037,026] | 0.903 |
| BMI | 29.4 10.0 | 29.0 9.2 | 0.393 |
| APR‐DRG Severity of Illness Score | 2.6 0.4 | 2.5 0.5 | 0.152 |
| Charlson Comorbidity Index | 6 [39] | 5 [27] | <0.001 |
| ICU transfer during admission | 93 (16.4) | 410 (16.7) | 0.842 |
| Myocardial infarction | 83 (14.6) | 256 (10.5) | 0.005 |
| Congestive heart failure | 177 (31.2) | 540 (22.1) | <0.001 |
| Peripheral vascular disease | 76 (13.4) | 214 (8.7) | 0.001 |
| Cardiovascular disease | 69 (12.2) | 224 (9.2) | 0.029 |
| Dementia | 15 (2.6) | 80 (3.3) | 0.445 |
| Chronic obstructive pulmonary disease | 220 (38.8) | 855 (34.9) | 0.083 |
| Connective tissue disease | 45 (7.9) | 118 (4.8) | 0.003 |
| Peptic ulcer disease | 26 (4.6) | 111 (4.5) | 0.958 |
| Cirrhosis | 60 (10.6) | 141 (5.8) | <0.001 |
| Diabetes mellitus without end‐organ complications | 148 (26.1) | 625 (25.5) | 0.779 |
| Diabetes mellitus with end‐organ complications | 92 (16.2) | 197 (8.0) | <0.001 |
| Paralysis | 25 (4.4) | 77 (3.1) | 0.134 |
| Renal failure | 214 (37.7) | 620 (25.3) | <0.001 |
| Underlying malignancy | 85 (15.0) | 314 (12.8) | 0.171 |
| Metastatic cancer | 64 (11.3) | 163 (6.7) | <0.001 |
| Human immunodeficiency virus | 10 (1.8) | 47 (1.9) | 0.806 |
| Minimum hemoglobin, g/dL | 9.1 [7.411.4] | 10.7 [8.712.4] | <0.001 |
| Minimum creatinine, mg/dL | 1.12 [0.792.35] | 1.03 [0.791.63] | 0.006 |
| Length of stay, d | 3.8 [1.97.8] | 3.3 [1.85.9] | <0.001 |
| ED visit in the past year | 1 [03] | 0 [01] | <0.001 |
| Clinical deterioration alert triggered | 269 (47.4) | 872 (35.6%) | <0.001 |
| Insurance | |||
| Private | 111 (19.6) | 528 (21.6) | 0.020 |
| Medicare | 299 (52.7) | 1,217 (49.7) | |
| Medicaid | 129 (22.8) | 499 (20.4) | |
| Patient pay | 28 (4.9) | 204 (8.3) | |
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).
| Variables | OR | 95% CI | P Value |
|---|---|---|---|
| |||
| Clinical deterioration alert | 1.40 | 1.261.55 | 0.001 |
| Age (1‐point increments) | 1.01 | 1.011.02 | 0.003 |
| Connective tissue disease | 1.63 | 1.341.98 | 0.012 |
| Cirrhosis | 1.25 | 1.171.33 | <0.001 |
| Diabetes mellitus with end‐organ complications | 1.23 | 1.131.33 | 0.010 |
| Chronic renal disease | 1.16 | 1.081.24 | 0.034 |
| Metastatic cancer | 1.12 | 1.081.17 | 0.002 |
| Emergency department visit in previous 6 months | 1.23 | 1.201.26 | <0.001 |

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]
Disclosure
This study was funded in part by the Barnes‐Jewish Hospital Foundation and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR or NIH.
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
Study Location
The study was conducted on 8 general medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri (January 15, 2015December 12, 2015). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or housestaff physicians under the supervision of an attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived.
Study Overview
We retrospectively evaluated all adult patients (aged >18 years) admitted through the emergency department or transferred directly to the general medicine units from other institutions. We excluded patients who died while hospitalized. All data were derived from the hospital informatics database provided by the Center for Clinical Excellence, BJC HealthCare.
Primary End Point
Readmission for any reason (ie, all‐cause readmission) to an acute care facility in the 30 days following discharge after the index hospitalization served as the primary end point. Barnes‐Jewish Hospital serves as the main teaching institution for BJC Healthcare, a large integrated healthcare system of both inpatient and outpatient care. The system includes a total of 12 hospitals and multiple community health locations in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 12 hospitals. If a patient who receives healthcare in the system presents to a nonsystem hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage. Patients with a 30‐day readmission were compared to those without a 30‐day readmission.
Variables
We recorded information regarding demographics, median income of the zip code of residence as a marker of socioeconomic status, admission to any BJC Healthcare facility within 6 months of the index admission, and comorbidities. To represent the global burden of comorbidities in each patient, we calculated their Charlson Comorbidity Index score.[12] Severity of illness was assessed using the All Patient RefinedDiagnosis Related Groups severity of illness score.
CDA Algorithm Overview
Details regarding the CDA model development and its implementation have been previously described in detail.[4, 5, 6] In brief, we applied logistic regression techniques to develop the CDA algorithm. Manually obtained vital signs, laboratory data, and pharmacy data inputted real time into the electronic medical record (EMR) were continuously assessed. The CDA algorithm searched for the 36 input variables (Table 1) as previously described from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week.[4, 5, 6] Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retain a sliding window of all the collected data points within the last 24 hours. We then subdivide these data into a series of n equally sized buckets (eg, 6 sequential buckets of 4 hours each). To capture variations within a bucket, we compute 3 values for each bucket: the minimum, maximum, and mean data points. Each of the resulting 3 n values are input to the logistic regression equation as separate variables.
| Age |
| Alanine aminotransferase |
| Alternative medicines |
| Anion gap |
| Anti‐infectives |
| Antineoplastics |
| Aspartate aminotransferase |
| Biologicals |
| Blood pressure, diastolic |
| Blood pressure, systolic |
| Calcium, serum |
| Calcium, serum, ionized |
| Cardiovascular agents |
| Central nervous system agents |
| Charlson Comorbidity Index |
| Coagulation modifiers |
| Estimated creatinine clearance |
| Gastrointestinal agents |
| Genitourinary tract agents |
| Hormones/hormone modifiers |
| Immunologic agents |
| Magnesium, serum |
| Metabolic agents |
| Miscellaneous agents |
| Nutritional products |
| Oxygen saturation, pulse oximetry |
| Phosphate, serum |
| Potassium, serum |
| Psychotherapeutic agents |
| Pulse |
| Radiologic agents |
| Respirations |
| Respiratory agents |
| Shock Index |
| Temperature |
| Topical agents |
The algorithm was first implemented in MATLAB (MathWorks, Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. The dataset's 36 input variables were divided into buckets and minimum/mean/maximum features wherever applicable, resulting in 398 variables. The first half of the original dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point the C statistic was 0.8834, with an overall accuracy of 0.9292.[5] Patients with inputted data meeting the CDA threshold had a real‐time alert sent to the hospital rapid response team prompting a patient evaluation.
Statistical Analysis
The number of patients admitted to the 8 general medicine units of Barnes‐Jewish Hospital during the study period determined the sample size. Categorical variables were compared using 2 or Fisher exact test as appropriate. Continuous variables were compared using the Mann‐Whitney U test. All analyses were 2‐tailed, and a P value of <0.05 was assumed to represent statistical significance. We relied on logistic regression for identifying variables independently associated with 30‐day readmission. Based on univariate analysis, variables significant at P < 0.15 were entered into the model. To arrive at the most parsimonious model, we utilized a stepwise backward elimination approach. We evaluated collinearity with the variance inflation factor. We report adjusted odds ratios (ORs) and 95% confidence intervals (CIs) where appropriate. The model's goodness of fit was assessed via calculation of the Hosmer‐Lemeshow test. Receiver operating characteristic (ROC) curves were used to compare the predictive models for 30‐day readmission with or without the CDA variable. All statistical analyses were performed using SPSS (version 22.0; IBM, Armonk, NY).
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.
| Variable | 30‐Day Readmission | P Value | |
|---|---|---|---|
| Yes (n = 567) | No (n = 2,448) | ||
| |||
| Age, y | 56.1 17.0 | 57.8 17.6 | 0.046 |
| Gender | |||
| Male | 252 (44.4) | 1,188 (48.5) | 0.079 |
| Female | 315 (55.6) | 1,260 (51.5) | |
| Race | |||
| Caucasian | 277 (48.9) | 1,234 (50.4) | 0.800 |
| African American | 257 (45.3) | 1,076 (44.0) | |
| Other | 33 (5.8) | 138 (5.6) | |
| Median income, dollars | 30,149 [25,23436,453] | 29,271 [24,83037,026] | 0.903 |
| BMI | 29.4 10.0 | 29.0 9.2 | 0.393 |
| APR‐DRG Severity of Illness Score | 2.6 0.4 | 2.5 0.5 | 0.152 |
| Charlson Comorbidity Index | 6 [39] | 5 [27] | <0.001 |
| ICU transfer during admission | 93 (16.4) | 410 (16.7) | 0.842 |
| Myocardial infarction | 83 (14.6) | 256 (10.5) | 0.005 |
| Congestive heart failure | 177 (31.2) | 540 (22.1) | <0.001 |
| Peripheral vascular disease | 76 (13.4) | 214 (8.7) | 0.001 |
| Cardiovascular disease | 69 (12.2) | 224 (9.2) | 0.029 |
| Dementia | 15 (2.6) | 80 (3.3) | 0.445 |
| Chronic obstructive pulmonary disease | 220 (38.8) | 855 (34.9) | 0.083 |
| Connective tissue disease | 45 (7.9) | 118 (4.8) | 0.003 |
| Peptic ulcer disease | 26 (4.6) | 111 (4.5) | 0.958 |
| Cirrhosis | 60 (10.6) | 141 (5.8) | <0.001 |
| Diabetes mellitus without end‐organ complications | 148 (26.1) | 625 (25.5) | 0.779 |
| Diabetes mellitus with end‐organ complications | 92 (16.2) | 197 (8.0) | <0.001 |
| Paralysis | 25 (4.4) | 77 (3.1) | 0.134 |
| Renal failure | 214 (37.7) | 620 (25.3) | <0.001 |
| Underlying malignancy | 85 (15.0) | 314 (12.8) | 0.171 |
| Metastatic cancer | 64 (11.3) | 163 (6.7) | <0.001 |
| Human immunodeficiency virus | 10 (1.8) | 47 (1.9) | 0.806 |
| Minimum hemoglobin, g/dL | 9.1 [7.411.4] | 10.7 [8.712.4] | <0.001 |
| Minimum creatinine, mg/dL | 1.12 [0.792.35] | 1.03 [0.791.63] | 0.006 |
| Length of stay, d | 3.8 [1.97.8] | 3.3 [1.85.9] | <0.001 |
| ED visit in the past year | 1 [03] | 0 [01] | <0.001 |
| Clinical deterioration alert triggered | 269 (47.4) | 872 (35.6%) | <0.001 |
| Insurance | |||
| Private | 111 (19.6) | 528 (21.6) | 0.020 |
| Medicare | 299 (52.7) | 1,217 (49.7) | |
| Medicaid | 129 (22.8) | 499 (20.4) | |
| Patient pay | 28 (4.9) | 204 (8.3) | |
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).
| Variables | OR | 95% CI | P Value |
|---|---|---|---|
| |||
| Clinical deterioration alert | 1.40 | 1.261.55 | 0.001 |
| Age (1‐point increments) | 1.01 | 1.011.02 | 0.003 |
| Connective tissue disease | 1.63 | 1.341.98 | 0.012 |
| Cirrhosis | 1.25 | 1.171.33 | <0.001 |
| Diabetes mellitus with end‐organ complications | 1.23 | 1.131.33 | 0.010 |
| Chronic renal disease | 1.16 | 1.081.24 | 0.034 |
| Metastatic cancer | 1.12 | 1.081.17 | 0.002 |
| Emergency department visit in previous 6 months | 1.23 | 1.201.26 | <0.001 |

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]
Disclosure
This study was funded in part by the Barnes‐Jewish Hospital Foundation and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR or NIH.
- , , . Rapid‐response teams. N Engl J Med. 2011;365:139–146.
- , , , , , . Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5:19–25.
- , , , et al. Implementation of a real‐time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39:469–473.
- , , , et al. Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
- , , , et al. A trial of a real‐time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013;8:236–242.
- , , , et al. A randomized trial of real‐time automated clinical deterioration alerts sent to a rapid response team. J Hosp Med. 2014;9:424–429.
- , . Revisiting hospital readmissions JAMA. 2013;309:398–400.
- , , , . Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7:224–230.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011; 155:520–528.
- , , , et al. International variation in and factors associated with hospital readmission after myocardial infarction. JAMA. 2012;307:66–74.
- , , , , . Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2014;11:685–694.
- , , , , , . Assessing illness severity: does clinical judgement work? J Chronic Dis. 1986;39:439–452.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48:981–988.
- , , , et al. The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30‐day readmission. J Hosp Med. 2013;8:689–695.
- , , . The Medicare hospital readmissions reduction program: time for reform. JAMA. 2015;314:347–348.
- , , . Rapid‐response teams. N Engl J Med. 2011;365:139–146.
- , , , , , . Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5:19–25.
- , , , et al. Implementation of a real‐time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39:469–473.
- , , , et al. Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
- , , , et al. A trial of a real‐time alert for clinical deterioration in patients hospitalized on general medical wards. J Hosp Med. 2013;8:236–242.
- , , , et al. A randomized trial of real‐time automated clinical deterioration alerts sent to a rapid response team. J Hosp Med. 2014;9:424–429.
- , . Revisiting hospital readmissions JAMA. 2013;309:398–400.
- , , , . Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7:224–230.
- , , , , . Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011; 155:520–528.
- , , , et al. International variation in and factors associated with hospital readmission after myocardial infarction. JAMA. 2012;307:66–74.
- , , , , . Predictors of early readmission among patients 40 to 64 years of age hospitalized for chronic obstructive pulmonary disease. Ann Am Thorac Soc. 2014;11:685–694.
- , , , , , . Assessing illness severity: does clinical judgement work? J Chronic Dis. 1986;39:439–452.
- , , , et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48:981–988.
- , , , et al. The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30‐day readmission. J Hosp Med. 2013;8:689–695.
- , , . The Medicare hospital readmissions reduction program: time for reform. JAMA. 2015;314:347–348.
MAGS Prevalence in Older Adults
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.

Phase 2: Prevalence of MAGS in Hospitalized Older Adults Discharged to SNFs
Sample
We next applied the MAGS list to a convenience sample of hospitalized patients discharged to SNFs to assess the prevalence of MAGS in this sample, and also to compare with the prevalence of Beers criteria[20] medications. Our sample was selected from data collected as part of a quality‐improvement project to reduce hospital readmissions in patients discharged to SNFs. The larger study enrolled a total 1093 medical and surgical patients who had Medicare insurance eligibility and were discharged from 1 large university hospital to 23 area SNFs from January 17, 2013 through July 31, 2014. The university institutional review board waived the requirement for written consent. For the purpose of this substudy. we selected the first 154 patients with complete chart abstraction (approximately 15% of the total) as a convenience sample.
Data Analysis
We applied descriptive statistics to summarize demographic and clinical characteristics of the convenience sample. To understand potential selection biases that could have resulted by the convenience sampling, we compared participant characteristics of the convenience sample (N = 154) with the characteristics of the remaining participants of the larger study (N = 939) using independent sample t tests and 2 tests for continuous and categorical measures, respectively. We applied the MAGS list and the AGS 2015 Beers criteria[20] for the sample of 154 and identified the medications associated with each of the 6 geriatric syndromes from the discharge medication lists completed by hospital clinical pharmacists. For each patient, we identified both scheduled and PRN (pro re nata, or as needed) medications associated with each geriatric syndrome. Thereafter, we determined whether the discharge list contained at least 1 medication associated with a geriatric syndrome per the MAGS list and the AGS Beers 2015 criteria,[20] and the percentage of overall medications that were part of the MAGS and Beers lists. Data were aggregated using means and standard deviations across syndromes (ie, number of discharge medications per syndrome per patient) along with the percentage of patients with 1 or more medications related to a specific syndrome and the percentage of medications that were MAGS. All analyses were performed using the SPSS statistical package (IBM SPSS Statistics for Windows, version 23.0; IBM, Armonk, NY).
RESULTS
Phase 1: MAGS List
The iterative process applied in this analysis generated a list of 513 medications associated with the 6 geriatric syndromes. The list of medications related to each syndrome and the corresponding rationale and relevant references for their inclusion is presented in the Supporting Information, Appendix 1, in the online version of this article. Table 1 summarizes these medications across 18 major drug categories. Antiepileptics were linked to all 6 geriatric syndromes, whereas antipsychotics, antidepressants, antiparkinsonism, and opioid agonists were associated with 5 syndromes. Ten of the 18 categories were associated with 3 geriatric syndromescognitive impairment, delirium, and falls. Four medication categories were associated with only 1 syndrome. Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics and nonopioid cough suppressant and expectorant medications were associated with falls syndrome only. Hormone replacement medications were associated with depression only, and immunosuppressants were associated with unintentional weight and appetite loss only.
| Major Medication Category | Delirium | Cognitive Impairment | Falls | Unintentional Weight and Appetite Loss | Urinary Incontinence | Depression | Drug Class/Drug Within Each Category |
|---|---|---|---|---|---|---|---|
| |||||||
| Antipsychotics | ✓ | ✓ | ✓ | ✓ | Atypical and typical antipsychotics, buspirone | ||
| Antidepressants | ✓ | ✓ | ✓ | ✓ | ✓ | Tricyclic and tetracyclic antidepressants, serotonin reuptake inhibitors, serotonin and norepinephrine reuptake inhibitor, aminoketone | |
| Antiepileptics | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Antiepileptics, mood stabilizers, barbiturates |
| Antiparkinsonism | ✓ | ✓ | ✓ | ✓ | ✓ | Aromatic amino acid decarboxylation inhibitor and catechol‐o‐methyltransferase inhibitor, catecholamine‐depleting sympatholytic, catechol‐o‐methyltransferase inhibitor, dopaminergic agonist, ergot derivative, monoamine oxidase inhibitor, nonergot dopamine agonist, | |
| Benzodiazapines | ✓ | ✓ | ✓ | Benzodiazapines only | |||
| Nonbenzodiazepine hypnotics | ✓ | ✓ | ✓ | Benzodiazepine analogs, nonbenzodiazepine hypnotics, tranquilizers, ‐aminobutyric acid A receptor agonist | |||
| Opioid agonists | ✓ | ✓ | ✓ | ✓ | ✓ | Full or partial opioid agonists, opiates, opioids | |
| Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics | ✓ | Nonopioid analgesics, NSAIDs, COX‐2 selective inhibitor NSAIDs | |||||
| Antihypertensives | ✓ | ✓ | ✓ | Calcium channel blocker, ‐adrenergic blocker, angiotensin‐converting enzyme inhibitor, angiotensin 2 receptor blocker, ‐adrenergic blocker, diuretics (loop, potassium sparing, thiazide), nitrate vasodilators, aldosterone blocker | |||
| Antiarrhythmic | ✓ | ✓ | ✓ | Antiarrhythmics, cardiac glycosides | |||
| Antidiabetics | ✓ | ✓ | Insulin and insulin analogs, sulfonylureas, ‐glucosidase inhibitor, amylin analog, biguanide, glinide, GLP‐1 receptor agonist, glucagon‐like peptide‐1 agonist | ||||
| Anticholinergics and/or antihistaminics | ✓ | ✓ | ✓ | ✓ | Anticholinergics, histamine receptor antagonists, muscarininc antagonists, combined anticholinergics, and histamine receptor antagonists | ||
| Antiemetics | ✓ | ✓ | ✓ | Antiemetics, dopaminergic antagonists, dopamine‐2 receptor antagonist | |||
| Hormone replacement | ✓ | Corticosteroids, progestin, estrogen, estrogen agonist/antagonist, gonadotropin releasing hormone receptor agonist | |||||
| Muscle relaxers | ✓ | ✓ | ✓ | ✓ | Muscle relaxers | ||
| Immunosuppressants | ✓ | Calcineurin inhibitor immunosuppressant, folate analog metabolic inhibitor, purine antimetabolite | |||||
| Nonopioid cough suppressants and expectorants | ✓ | Expectorant, non‐narcotic antitussive, ‐1 agonist, uncompetitive N‐methyl‐D‐aspartate receptor antagonist | |||||
| Antimicrobials | ✓ | ✓ | Macrolide, cephalosporin, penicillin class, rifamycin, non‐nucleoside analog reverse transcriptase inhibitor, influenza A M2 protein inhibitor | ||||
| Others | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ‐3‐adrenergic agonist, methylxanthine, cholinesterase inhibitor, interferon and , partial cholinergic nicotinic agonist, tyrosine hydroxylase, retinoid, serotonin‐1b and serotonin‐1d receptor agonist, stimulant laxative, vitamin K antagonist, platelet aggregation inhibitor |
Approximately 58% of the medications overlapped with the AGS 2015 Beer's Criteria[20] irrespective of whether the specific syndrome association was stated in the rationale.[20] Medications that overlapped were mostly in the delirium, cognitive impairment, and falls category with only a few overlaps in depression, unintentional weight loss, and urinary incontinence lists (see Supporting Information, Appendix 1, in the online version of this article).
Phase 2: Prevalence of MAGS
Among 154 participants, the mean age was 76.5 (10.6) years, 64.3% were female, 77.9% were white, and 96.1% non‐Hispanic. The median hospital length of stay was 6 days, with an interquartile range of 5 days. The orthopedic service discharged the highest proportion of patients (24%), followed by the geriatrics and internal medicine services, which each discharged 19.5% of the patients (Table 2). The remaining participants of the larger quality‐improvement project (N = 939) did not significantly differ on these demographic and clinical characteristics except for hospital length of stay, which was shorter in the sample analyzed (see Supporting Information, Appendix 2, in the online version of this article).
| Baseline Characteristics | Mean ( SD) or Percent (n) |
|---|---|
| |
| Age, y | 76.5 ( 10.6) |
| Sex | |
| Female | 64.3% (99) |
| Race | |
| White | 77.9% (126) |
| Black | 16.2% (25) |
| Unknown | 0.6% (1) |
| Declined | 0.6% (1) |
| Missing | 0.6% (1) |
| Ethnicity | |
| Non‐Hispanic | 96.1% (148) |
| Hispanic | 1.3% (2) |
| Unknown | 2.6% (4) |
| Hospital length of stay, d | 7.0 ( 4.2) |
| Hospital length of stay, d, median (IQR) | 6.0 (5.0) |
| No. of hospital discharge medications, count | 14.0 ( 4.7) |
| Discharge service | |
| Orthopedic service | 24.0% (37) |
| Geriatric service | 19.5% (30) |
| Internal medicine | 19.5% (30) |
| Other | 37.0% (57) |
Patients were discharged to SNFs with an average of 14.0 (4.7) medication orders. Overall, 43% (13%) of these discharge medication orders were MAGS. Every patient in the sample was ordered at least 1 medication associated with geriatric syndromes. Multiple MAGS were the norm, with an average of 5.9 (2.2) MAGS per patient. MAGS were also the norm, as 98.1% of the sample had medication orders associated with at least 2 different syndromes.
When the Beer's criteria[20] were applied to the medication orders (instead of the MAGS list), problematic medications appeared less common. Patients had an average of 3.04 (1.7) MAGS that were also listed on the AGS 2015 Beer's list,[20] representing an average of 22.3% of all discharge orders.
Table 3 illustrates the average number of medications per patient associated with each syndrome, and the percentage of patients (number in parentheses) discharged with at least 1 medication associated with each syndrome per the MAGS list and the Beers 2015 criteria.[20] For example, per the MAGS list, the syndrome most frequently associated with medications was falls, with patients discharged on an average of 5.5 (2.2) medications associated with falls, and 100% of the sample had at least 1 discharge medication associated with falls. Alternatively, the syndrome associated with the lowest frequency of medications was unintentional weight loss (with an average of 0.38 medications per patient), although 36% of these patients had more than 1 discharge medication associated with weight loss. As seen in Table 3, the mean and prevalence of 1 or more medications associated with each of the geriatric syndromes as identified by the Beers 2015 criteria[20] was lower than those identified by the MAGS list developed for this study.
| Geriatric Syndromes | Associated Medications per MAGS List | Associated Medications per AGS Beers 2015 Criteria | ||
|---|---|---|---|---|
| Mean SD | Percentage of Patients Receiving 1 Related Medication | Mean SD | Percentage of Patients Receiving 1 Related Medication | |
| ||||
| Cognitive impairment | 1.8 ( 1.2) | 84.4% (130) | 1.6 ( 1.2) | 78.6% (121) |
| Delirium | 1.4 ( 1.1) | 76.0% (117) | 1.3 ( 1.2) | 68.2% (105) |
| Falls | 5.5 ( 2.2) | 100% (154) | 2.6 ( 1.6) | 92.2% (142) |
| Unintentional weight and/or appetite loss | 0.4 ( 0.5) | 36.3% (56) | 0.1 ( 0.3) | 6.5% (10) |
| Urinary incontinence | 1.6 ( 1.0) | 85.7% (132) | 0.1 ( 0.2) | 5.8% (9) |
| Depression | 1.7 ( 1.0) | 90.9% (140) | 0.0 ( 0.0) | 0.0% (0) |
| All syndromes | 5.9 ( 2.2) | 100% (154) | 3.0 ( 1.7) | 95% (149) |
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.
Acknowledgements
The authors thank Dr. Linda Beuscher, Dr. Patricia Blair Miller, Dr. Joseph Ouslander, Dr. William Stuart Reynolds, and Dr. Warren Taylor for providing their expertise and participating in the expert panel discussions that facilitated the development of the MAGS list. The authors also recognize the research support provided by Christopher Simon Coelho.
Disclosures: This research was supported by the Department of Health and Human Services, Centers for Medicare & Medicaid Services grant #1C1CMS331006 awarded to Principal Investigator, John F. Schnelle, PhD. Dr. Vasilevskis was supported by the National Institute on Aging of the National Institutes of Health award K23AG040157 and the Geriatric Research, Education and Clinical Center. Dr. Bell was supported by National Institute on Aging‐K award K23AG048347‐01A1. Dr. Mixon is supported by a Veterans Affairs Health Services Research & Development Career Development Award (12‐168). This research was also supported by the National Center for Advancing Translational Sciences Clinical and Translational Science award UL1TR000445. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies, the National Center for Advancing Translation Science, the National Institutes of Health, or the Department of Veterans Affairs. Each coauthor contributed significantly to the manuscript. Dr. Kripalani has received stock/stock options from Bioscape Digital, LLC. None of the other authors have significant conflicts of interest to report related to this project or the results reported within this article.
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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.

Phase 2: Prevalence of MAGS in Hospitalized Older Adults Discharged to SNFs
Sample
We next applied the MAGS list to a convenience sample of hospitalized patients discharged to SNFs to assess the prevalence of MAGS in this sample, and also to compare with the prevalence of Beers criteria[20] medications. Our sample was selected from data collected as part of a quality‐improvement project to reduce hospital readmissions in patients discharged to SNFs. The larger study enrolled a total 1093 medical and surgical patients who had Medicare insurance eligibility and were discharged from 1 large university hospital to 23 area SNFs from January 17, 2013 through July 31, 2014. The university institutional review board waived the requirement for written consent. For the purpose of this substudy. we selected the first 154 patients with complete chart abstraction (approximately 15% of the total) as a convenience sample.
Data Analysis
We applied descriptive statistics to summarize demographic and clinical characteristics of the convenience sample. To understand potential selection biases that could have resulted by the convenience sampling, we compared participant characteristics of the convenience sample (N = 154) with the characteristics of the remaining participants of the larger study (N = 939) using independent sample t tests and 2 tests for continuous and categorical measures, respectively. We applied the MAGS list and the AGS 2015 Beers criteria[20] for the sample of 154 and identified the medications associated with each of the 6 geriatric syndromes from the discharge medication lists completed by hospital clinical pharmacists. For each patient, we identified both scheduled and PRN (pro re nata, or as needed) medications associated with each geriatric syndrome. Thereafter, we determined whether the discharge list contained at least 1 medication associated with a geriatric syndrome per the MAGS list and the AGS Beers 2015 criteria,[20] and the percentage of overall medications that were part of the MAGS and Beers lists. Data were aggregated using means and standard deviations across syndromes (ie, number of discharge medications per syndrome per patient) along with the percentage of patients with 1 or more medications related to a specific syndrome and the percentage of medications that were MAGS. All analyses were performed using the SPSS statistical package (IBM SPSS Statistics for Windows, version 23.0; IBM, Armonk, NY).
RESULTS
Phase 1: MAGS List
The iterative process applied in this analysis generated a list of 513 medications associated with the 6 geriatric syndromes. The list of medications related to each syndrome and the corresponding rationale and relevant references for their inclusion is presented in the Supporting Information, Appendix 1, in the online version of this article. Table 1 summarizes these medications across 18 major drug categories. Antiepileptics were linked to all 6 geriatric syndromes, whereas antipsychotics, antidepressants, antiparkinsonism, and opioid agonists were associated with 5 syndromes. Ten of the 18 categories were associated with 3 geriatric syndromescognitive impairment, delirium, and falls. Four medication categories were associated with only 1 syndrome. Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics and nonopioid cough suppressant and expectorant medications were associated with falls syndrome only. Hormone replacement medications were associated with depression only, and immunosuppressants were associated with unintentional weight and appetite loss only.
| Major Medication Category | Delirium | Cognitive Impairment | Falls | Unintentional Weight and Appetite Loss | Urinary Incontinence | Depression | Drug Class/Drug Within Each Category |
|---|---|---|---|---|---|---|---|
| |||||||
| Antipsychotics | ✓ | ✓ | ✓ | ✓ | Atypical and typical antipsychotics, buspirone | ||
| Antidepressants | ✓ | ✓ | ✓ | ✓ | ✓ | Tricyclic and tetracyclic antidepressants, serotonin reuptake inhibitors, serotonin and norepinephrine reuptake inhibitor, aminoketone | |
| Antiepileptics | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Antiepileptics, mood stabilizers, barbiturates |
| Antiparkinsonism | ✓ | ✓ | ✓ | ✓ | ✓ | Aromatic amino acid decarboxylation inhibitor and catechol‐o‐methyltransferase inhibitor, catecholamine‐depleting sympatholytic, catechol‐o‐methyltransferase inhibitor, dopaminergic agonist, ergot derivative, monoamine oxidase inhibitor, nonergot dopamine agonist, | |
| Benzodiazapines | ✓ | ✓ | ✓ | Benzodiazapines only | |||
| Nonbenzodiazepine hypnotics | ✓ | ✓ | ✓ | Benzodiazepine analogs, nonbenzodiazepine hypnotics, tranquilizers, ‐aminobutyric acid A receptor agonist | |||
| Opioid agonists | ✓ | ✓ | ✓ | ✓ | ✓ | Full or partial opioid agonists, opiates, opioids | |
| Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics | ✓ | Nonopioid analgesics, NSAIDs, COX‐2 selective inhibitor NSAIDs | |||||
| Antihypertensives | ✓ | ✓ | ✓ | Calcium channel blocker, ‐adrenergic blocker, angiotensin‐converting enzyme inhibitor, angiotensin 2 receptor blocker, ‐adrenergic blocker, diuretics (loop, potassium sparing, thiazide), nitrate vasodilators, aldosterone blocker | |||
| Antiarrhythmic | ✓ | ✓ | ✓ | Antiarrhythmics, cardiac glycosides | |||
| Antidiabetics | ✓ | ✓ | Insulin and insulin analogs, sulfonylureas, ‐glucosidase inhibitor, amylin analog, biguanide, glinide, GLP‐1 receptor agonist, glucagon‐like peptide‐1 agonist | ||||
| Anticholinergics and/or antihistaminics | ✓ | ✓ | ✓ | ✓ | Anticholinergics, histamine receptor antagonists, muscarininc antagonists, combined anticholinergics, and histamine receptor antagonists | ||
| Antiemetics | ✓ | ✓ | ✓ | Antiemetics, dopaminergic antagonists, dopamine‐2 receptor antagonist | |||
| Hormone replacement | ✓ | Corticosteroids, progestin, estrogen, estrogen agonist/antagonist, gonadotropin releasing hormone receptor agonist | |||||
| Muscle relaxers | ✓ | ✓ | ✓ | ✓ | Muscle relaxers | ||
| Immunosuppressants | ✓ | Calcineurin inhibitor immunosuppressant, folate analog metabolic inhibitor, purine antimetabolite | |||||
| Nonopioid cough suppressants and expectorants | ✓ | Expectorant, non‐narcotic antitussive, ‐1 agonist, uncompetitive N‐methyl‐D‐aspartate receptor antagonist | |||||
| Antimicrobials | ✓ | ✓ | Macrolide, cephalosporin, penicillin class, rifamycin, non‐nucleoside analog reverse transcriptase inhibitor, influenza A M2 protein inhibitor | ||||
| Others | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ‐3‐adrenergic agonist, methylxanthine, cholinesterase inhibitor, interferon and , partial cholinergic nicotinic agonist, tyrosine hydroxylase, retinoid, serotonin‐1b and serotonin‐1d receptor agonist, stimulant laxative, vitamin K antagonist, platelet aggregation inhibitor |
Approximately 58% of the medications overlapped with the AGS 2015 Beer's Criteria[20] irrespective of whether the specific syndrome association was stated in the rationale.[20] Medications that overlapped were mostly in the delirium, cognitive impairment, and falls category with only a few overlaps in depression, unintentional weight loss, and urinary incontinence lists (see Supporting Information, Appendix 1, in the online version of this article).
Phase 2: Prevalence of MAGS
Among 154 participants, the mean age was 76.5 (10.6) years, 64.3% were female, 77.9% were white, and 96.1% non‐Hispanic. The median hospital length of stay was 6 days, with an interquartile range of 5 days. The orthopedic service discharged the highest proportion of patients (24%), followed by the geriatrics and internal medicine services, which each discharged 19.5% of the patients (Table 2). The remaining participants of the larger quality‐improvement project (N = 939) did not significantly differ on these demographic and clinical characteristics except for hospital length of stay, which was shorter in the sample analyzed (see Supporting Information, Appendix 2, in the online version of this article).
| Baseline Characteristics | Mean ( SD) or Percent (n) |
|---|---|
| |
| Age, y | 76.5 ( 10.6) |
| Sex | |
| Female | 64.3% (99) |
| Race | |
| White | 77.9% (126) |
| Black | 16.2% (25) |
| Unknown | 0.6% (1) |
| Declined | 0.6% (1) |
| Missing | 0.6% (1) |
| Ethnicity | |
| Non‐Hispanic | 96.1% (148) |
| Hispanic | 1.3% (2) |
| Unknown | 2.6% (4) |
| Hospital length of stay, d | 7.0 ( 4.2) |
| Hospital length of stay, d, median (IQR) | 6.0 (5.0) |
| No. of hospital discharge medications, count | 14.0 ( 4.7) |
| Discharge service | |
| Orthopedic service | 24.0% (37) |
| Geriatric service | 19.5% (30) |
| Internal medicine | 19.5% (30) |
| Other | 37.0% (57) |
Patients were discharged to SNFs with an average of 14.0 (4.7) medication orders. Overall, 43% (13%) of these discharge medication orders were MAGS. Every patient in the sample was ordered at least 1 medication associated with geriatric syndromes. Multiple MAGS were the norm, with an average of 5.9 (2.2) MAGS per patient. MAGS were also the norm, as 98.1% of the sample had medication orders associated with at least 2 different syndromes.
When the Beer's criteria[20] were applied to the medication orders (instead of the MAGS list), problematic medications appeared less common. Patients had an average of 3.04 (1.7) MAGS that were also listed on the AGS 2015 Beer's list,[20] representing an average of 22.3% of all discharge orders.
Table 3 illustrates the average number of medications per patient associated with each syndrome, and the percentage of patients (number in parentheses) discharged with at least 1 medication associated with each syndrome per the MAGS list and the Beers 2015 criteria.[20] For example, per the MAGS list, the syndrome most frequently associated with medications was falls, with patients discharged on an average of 5.5 (2.2) medications associated with falls, and 100% of the sample had at least 1 discharge medication associated with falls. Alternatively, the syndrome associated with the lowest frequency of medications was unintentional weight loss (with an average of 0.38 medications per patient), although 36% of these patients had more than 1 discharge medication associated with weight loss. As seen in Table 3, the mean and prevalence of 1 or more medications associated with each of the geriatric syndromes as identified by the Beers 2015 criteria[20] was lower than those identified by the MAGS list developed for this study.
| Geriatric Syndromes | Associated Medications per MAGS List | Associated Medications per AGS Beers 2015 Criteria | ||
|---|---|---|---|---|
| Mean SD | Percentage of Patients Receiving 1 Related Medication | Mean SD | Percentage of Patients Receiving 1 Related Medication | |
| ||||
| Cognitive impairment | 1.8 ( 1.2) | 84.4% (130) | 1.6 ( 1.2) | 78.6% (121) |
| Delirium | 1.4 ( 1.1) | 76.0% (117) | 1.3 ( 1.2) | 68.2% (105) |
| Falls | 5.5 ( 2.2) | 100% (154) | 2.6 ( 1.6) | 92.2% (142) |
| Unintentional weight and/or appetite loss | 0.4 ( 0.5) | 36.3% (56) | 0.1 ( 0.3) | 6.5% (10) |
| Urinary incontinence | 1.6 ( 1.0) | 85.7% (132) | 0.1 ( 0.2) | 5.8% (9) |
| Depression | 1.7 ( 1.0) | 90.9% (140) | 0.0 ( 0.0) | 0.0% (0) |
| All syndromes | 5.9 ( 2.2) | 100% (154) | 3.0 ( 1.7) | 95% (149) |
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.
Acknowledgements
The authors thank Dr. Linda Beuscher, Dr. Patricia Blair Miller, Dr. Joseph Ouslander, Dr. William Stuart Reynolds, and Dr. Warren Taylor for providing their expertise and participating in the expert panel discussions that facilitated the development of the MAGS list. The authors also recognize the research support provided by Christopher Simon Coelho.
Disclosures: This research was supported by the Department of Health and Human Services, Centers for Medicare & Medicaid Services grant #1C1CMS331006 awarded to Principal Investigator, John F. Schnelle, PhD. Dr. Vasilevskis was supported by the National Institute on Aging of the National Institutes of Health award K23AG040157 and the Geriatric Research, Education and Clinical Center. Dr. Bell was supported by National Institute on Aging‐K award K23AG048347‐01A1. Dr. Mixon is supported by a Veterans Affairs Health Services Research & Development Career Development Award (12‐168). This research was also supported by the National Center for Advancing Translational Sciences Clinical and Translational Science award UL1TR000445. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies, the National Center for Advancing Translation Science, the National Institutes of Health, or the Department of Veterans Affairs. Each coauthor contributed significantly to the manuscript. Dr. Kripalani has received stock/stock options from Bioscape Digital, LLC. None of the other authors have significant conflicts of interest to report related to this project or the results reported within this article.
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.

Phase 2: Prevalence of MAGS in Hospitalized Older Adults Discharged to SNFs
Sample
We next applied the MAGS list to a convenience sample of hospitalized patients discharged to SNFs to assess the prevalence of MAGS in this sample, and also to compare with the prevalence of Beers criteria[20] medications. Our sample was selected from data collected as part of a quality‐improvement project to reduce hospital readmissions in patients discharged to SNFs. The larger study enrolled a total 1093 medical and surgical patients who had Medicare insurance eligibility and were discharged from 1 large university hospital to 23 area SNFs from January 17, 2013 through July 31, 2014. The university institutional review board waived the requirement for written consent. For the purpose of this substudy. we selected the first 154 patients with complete chart abstraction (approximately 15% of the total) as a convenience sample.
Data Analysis
We applied descriptive statistics to summarize demographic and clinical characteristics of the convenience sample. To understand potential selection biases that could have resulted by the convenience sampling, we compared participant characteristics of the convenience sample (N = 154) with the characteristics of the remaining participants of the larger study (N = 939) using independent sample t tests and 2 tests for continuous and categorical measures, respectively. We applied the MAGS list and the AGS 2015 Beers criteria[20] for the sample of 154 and identified the medications associated with each of the 6 geriatric syndromes from the discharge medication lists completed by hospital clinical pharmacists. For each patient, we identified both scheduled and PRN (pro re nata, or as needed) medications associated with each geriatric syndrome. Thereafter, we determined whether the discharge list contained at least 1 medication associated with a geriatric syndrome per the MAGS list and the AGS Beers 2015 criteria,[20] and the percentage of overall medications that were part of the MAGS and Beers lists. Data were aggregated using means and standard deviations across syndromes (ie, number of discharge medications per syndrome per patient) along with the percentage of patients with 1 or more medications related to a specific syndrome and the percentage of medications that were MAGS. All analyses were performed using the SPSS statistical package (IBM SPSS Statistics for Windows, version 23.0; IBM, Armonk, NY).
RESULTS
Phase 1: MAGS List
The iterative process applied in this analysis generated a list of 513 medications associated with the 6 geriatric syndromes. The list of medications related to each syndrome and the corresponding rationale and relevant references for their inclusion is presented in the Supporting Information, Appendix 1, in the online version of this article. Table 1 summarizes these medications across 18 major drug categories. Antiepileptics were linked to all 6 geriatric syndromes, whereas antipsychotics, antidepressants, antiparkinsonism, and opioid agonists were associated with 5 syndromes. Ten of the 18 categories were associated with 3 geriatric syndromescognitive impairment, delirium, and falls. Four medication categories were associated with only 1 syndrome. Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics and nonopioid cough suppressant and expectorant medications were associated with falls syndrome only. Hormone replacement medications were associated with depression only, and immunosuppressants were associated with unintentional weight and appetite loss only.
| Major Medication Category | Delirium | Cognitive Impairment | Falls | Unintentional Weight and Appetite Loss | Urinary Incontinence | Depression | Drug Class/Drug Within Each Category |
|---|---|---|---|---|---|---|---|
| |||||||
| Antipsychotics | ✓ | ✓ | ✓ | ✓ | Atypical and typical antipsychotics, buspirone | ||
| Antidepressants | ✓ | ✓ | ✓ | ✓ | ✓ | Tricyclic and tetracyclic antidepressants, serotonin reuptake inhibitors, serotonin and norepinephrine reuptake inhibitor, aminoketone | |
| Antiepileptics | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Antiepileptics, mood stabilizers, barbiturates |
| Antiparkinsonism | ✓ | ✓ | ✓ | ✓ | ✓ | Aromatic amino acid decarboxylation inhibitor and catechol‐o‐methyltransferase inhibitor, catecholamine‐depleting sympatholytic, catechol‐o‐methyltransferase inhibitor, dopaminergic agonist, ergot derivative, monoamine oxidase inhibitor, nonergot dopamine agonist, | |
| Benzodiazapines | ✓ | ✓ | ✓ | Benzodiazapines only | |||
| Nonbenzodiazepine hypnotics | ✓ | ✓ | ✓ | Benzodiazepine analogs, nonbenzodiazepine hypnotics, tranquilizers, ‐aminobutyric acid A receptor agonist | |||
| Opioid agonists | ✓ | ✓ | ✓ | ✓ | ✓ | Full or partial opioid agonists, opiates, opioids | |
| Nonopioid/nonsteroidal anti‐inflammatory and/or analgesics | ✓ | Nonopioid analgesics, NSAIDs, COX‐2 selective inhibitor NSAIDs | |||||
| Antihypertensives | ✓ | ✓ | ✓ | Calcium channel blocker, ‐adrenergic blocker, angiotensin‐converting enzyme inhibitor, angiotensin 2 receptor blocker, ‐adrenergic blocker, diuretics (loop, potassium sparing, thiazide), nitrate vasodilators, aldosterone blocker | |||
| Antiarrhythmic | ✓ | ✓ | ✓ | Antiarrhythmics, cardiac glycosides | |||
| Antidiabetics | ✓ | ✓ | Insulin and insulin analogs, sulfonylureas, ‐glucosidase inhibitor, amylin analog, biguanide, glinide, GLP‐1 receptor agonist, glucagon‐like peptide‐1 agonist | ||||
| Anticholinergics and/or antihistaminics | ✓ | ✓ | ✓ | ✓ | Anticholinergics, histamine receptor antagonists, muscarininc antagonists, combined anticholinergics, and histamine receptor antagonists | ||
| Antiemetics | ✓ | ✓ | ✓ | Antiemetics, dopaminergic antagonists, dopamine‐2 receptor antagonist | |||
| Hormone replacement | ✓ | Corticosteroids, progestin, estrogen, estrogen agonist/antagonist, gonadotropin releasing hormone receptor agonist | |||||
| Muscle relaxers | ✓ | ✓ | ✓ | ✓ | Muscle relaxers | ||
| Immunosuppressants | ✓ | Calcineurin inhibitor immunosuppressant, folate analog metabolic inhibitor, purine antimetabolite | |||||
| Nonopioid cough suppressants and expectorants | ✓ | Expectorant, non‐narcotic antitussive, ‐1 agonist, uncompetitive N‐methyl‐D‐aspartate receptor antagonist | |||||
| Antimicrobials | ✓ | ✓ | Macrolide, cephalosporin, penicillin class, rifamycin, non‐nucleoside analog reverse transcriptase inhibitor, influenza A M2 protein inhibitor | ||||
| Others | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ‐3‐adrenergic agonist, methylxanthine, cholinesterase inhibitor, interferon and , partial cholinergic nicotinic agonist, tyrosine hydroxylase, retinoid, serotonin‐1b and serotonin‐1d receptor agonist, stimulant laxative, vitamin K antagonist, platelet aggregation inhibitor |
Approximately 58% of the medications overlapped with the AGS 2015 Beer's Criteria[20] irrespective of whether the specific syndrome association was stated in the rationale.[20] Medications that overlapped were mostly in the delirium, cognitive impairment, and falls category with only a few overlaps in depression, unintentional weight loss, and urinary incontinence lists (see Supporting Information, Appendix 1, in the online version of this article).
Phase 2: Prevalence of MAGS
Among 154 participants, the mean age was 76.5 (10.6) years, 64.3% were female, 77.9% were white, and 96.1% non‐Hispanic. The median hospital length of stay was 6 days, with an interquartile range of 5 days. The orthopedic service discharged the highest proportion of patients (24%), followed by the geriatrics and internal medicine services, which each discharged 19.5% of the patients (Table 2). The remaining participants of the larger quality‐improvement project (N = 939) did not significantly differ on these demographic and clinical characteristics except for hospital length of stay, which was shorter in the sample analyzed (see Supporting Information, Appendix 2, in the online version of this article).
| Baseline Characteristics | Mean ( SD) or Percent (n) |
|---|---|
| |
| Age, y | 76.5 ( 10.6) |
| Sex | |
| Female | 64.3% (99) |
| Race | |
| White | 77.9% (126) |
| Black | 16.2% (25) |
| Unknown | 0.6% (1) |
| Declined | 0.6% (1) |
| Missing | 0.6% (1) |
| Ethnicity | |
| Non‐Hispanic | 96.1% (148) |
| Hispanic | 1.3% (2) |
| Unknown | 2.6% (4) |
| Hospital length of stay, d | 7.0 ( 4.2) |
| Hospital length of stay, d, median (IQR) | 6.0 (5.0) |
| No. of hospital discharge medications, count | 14.0 ( 4.7) |
| Discharge service | |
| Orthopedic service | 24.0% (37) |
| Geriatric service | 19.5% (30) |
| Internal medicine | 19.5% (30) |
| Other | 37.0% (57) |
Patients were discharged to SNFs with an average of 14.0 (4.7) medication orders. Overall, 43% (13%) of these discharge medication orders were MAGS. Every patient in the sample was ordered at least 1 medication associated with geriatric syndromes. Multiple MAGS were the norm, with an average of 5.9 (2.2) MAGS per patient. MAGS were also the norm, as 98.1% of the sample had medication orders associated with at least 2 different syndromes.
When the Beer's criteria[20] were applied to the medication orders (instead of the MAGS list), problematic medications appeared less common. Patients had an average of 3.04 (1.7) MAGS that were also listed on the AGS 2015 Beer's list,[20] representing an average of 22.3% of all discharge orders.
Table 3 illustrates the average number of medications per patient associated with each syndrome, and the percentage of patients (number in parentheses) discharged with at least 1 medication associated with each syndrome per the MAGS list and the Beers 2015 criteria.[20] For example, per the MAGS list, the syndrome most frequently associated with medications was falls, with patients discharged on an average of 5.5 (2.2) medications associated with falls, and 100% of the sample had at least 1 discharge medication associated with falls. Alternatively, the syndrome associated with the lowest frequency of medications was unintentional weight loss (with an average of 0.38 medications per patient), although 36% of these patients had more than 1 discharge medication associated with weight loss. As seen in Table 3, the mean and prevalence of 1 or more medications associated with each of the geriatric syndromes as identified by the Beers 2015 criteria[20] was lower than those identified by the MAGS list developed for this study.
| Geriatric Syndromes | Associated Medications per MAGS List | Associated Medications per AGS Beers 2015 Criteria | ||
|---|---|---|---|---|
| Mean SD | Percentage of Patients Receiving 1 Related Medication | Mean SD | Percentage of Patients Receiving 1 Related Medication | |
| ||||
| Cognitive impairment | 1.8 ( 1.2) | 84.4% (130) | 1.6 ( 1.2) | 78.6% (121) |
| Delirium | 1.4 ( 1.1) | 76.0% (117) | 1.3 ( 1.2) | 68.2% (105) |
| Falls | 5.5 ( 2.2) | 100% (154) | 2.6 ( 1.6) | 92.2% (142) |
| Unintentional weight and/or appetite loss | 0.4 ( 0.5) | 36.3% (56) | 0.1 ( 0.3) | 6.5% (10) |
| Urinary incontinence | 1.6 ( 1.0) | 85.7% (132) | 0.1 ( 0.2) | 5.8% (9) |
| Depression | 1.7 ( 1.0) | 90.9% (140) | 0.0 ( 0.0) | 0.0% (0) |
| All syndromes | 5.9 ( 2.2) | 100% (154) | 3.0 ( 1.7) | 95% (149) |
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.
Acknowledgements
The authors thank Dr. Linda Beuscher, Dr. Patricia Blair Miller, Dr. Joseph Ouslander, Dr. William Stuart Reynolds, and Dr. Warren Taylor for providing their expertise and participating in the expert panel discussions that facilitated the development of the MAGS list. The authors also recognize the research support provided by Christopher Simon Coelho.
Disclosures: This research was supported by the Department of Health and Human Services, Centers for Medicare & Medicaid Services grant #1C1CMS331006 awarded to Principal Investigator, John F. Schnelle, PhD. Dr. Vasilevskis was supported by the National Institute on Aging of the National Institutes of Health award K23AG040157 and the Geriatric Research, Education and Clinical Center. Dr. Bell was supported by National Institute on Aging‐K award K23AG048347‐01A1. Dr. Mixon is supported by a Veterans Affairs Health Services Research & Development Career Development Award (12‐168). This research was also supported by the National Center for Advancing Translational Sciences Clinical and Translational Science award UL1TR000445. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies, the National Center for Advancing Translation Science, the National Institutes of Health, or the Department of Veterans Affairs. Each coauthor contributed significantly to the manuscript. Dr. Kripalani has received stock/stock options from Bioscape Digital, LLC. None of the other authors have significant conflicts of interest to report related to this project or the results reported within this article.
- , , , . Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55:780–791.
- , , , . Shared risk factors for falls, incontinence, and functional dependence. Unifying the approach to geriatric syndromes. JAMA. 1995;273:1348–1353.
- , , , . Geriatric syndromes: medical misnomer or progress in geriatrics? Neth J Med. 2003;61:83–87.
- , , , et al. Geriatric conditions in acutely hospitalized older patients: prevalence and one‐year survival and functional decline. PLoS One. 2011;6:e26951.
- , , , , . Geriatric conditions as predictors of increased number of hospital admissions and hospital bed days over one year: findings of a nationwide cohort of older adults from Taiwan. Arch Gerontol Geriatr. 2014;59:169–174.
- , , , , . Geriatric conditions and disability: the Health and Retirement Study. Ann Intern Med. 2007;147:156–164.
- , , , , , . A prospective cohort study of geriatric syndromes among older medical patients admitted to acute care hospitals. J Am Geriatr Soc. 2011;59:2001–2008.
- , , , et al. Geriatric syndromes in hospitalized older adults discharged to skilled nursing facilities. J Am Geriatr Soc. 2016;64(4):715–722.
- , , , et al. Discharge to a skilled nursing facility and subsequent clinical outcomes among older patients hospitalized for heart failure. Circ Heart Fail. 2011;4:293–300.
- . Hazards of hospitalization of the elderly. Ann Intern Med. 1993;118:219–223.
- , , , , . Geriatric syndromes in elderly patients admitted to an inpatient cardiology ward. J Hosp Med. 2007;2:394–400.
- , , , . Effect of hospitalization on inappropriate prescribing in elderly Medicare beneficiaries. J Am Geriatr Soc. 2015;63:699–707.
- , , , et al. Polypharmacy cutoff and outcomes: five or more medicines were used to identify community‐dwelling older men at risk of different adverse outcomes. J Clin Epidemiol. 2012;65:989–995.
- , , , , . Investigating polypharmacy and drug burden index in hospitalised older people. Intern Med J. 2013;43:912–918.
- , . Potentially harmful drug‐drug interactions in the elderly: a review. Am J Geriatr Pharmacother. 2011;9:364–377.
- , , , . Hospital admissions/visits associated with drug‐drug interactions: a systematic review and meta‐analysis. Pharmacoepidemiol Drug Saf. 2014;23:489–497.
- , . Optimising drug treatment for elderly people: the prescribing cascade. BMJ. 1997;315:1096–1099.
- , , , et al. Association between acute geriatric syndromes and medication‐related hospital admissions. Drugs Aging. 2012;29:691–699.
- American Geriatrics Society Beers Criteria Update Expert P. American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2012;60:616–631.
- By the American Geriatrics Society 2015 Beers Criteria Update Expert Panel. American Geriatrics Society 2015 updated Beers criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2015;63:2227–2246.
- , . STOPP (Screening Tool of Older Persons' potentially inappropriate Prescriptions): application to acutely ill elderly patients and comparison with Beers' criteria. Age Ageing. 2008;37:673–679.
- , , , et al. Warfarin versus aspirin for stroke prevention in an elderly community population with atrial fibrillation (the Birmingham Atrial Fibrillation Treatment of the Aged Study, BAFTA): a randomised controlled trial. Lancet. 2007;370:493–503.
- U.S. Food and Drug Administration. Drugs. Available at: http://www.fda.gov/Drugs/default.htm. Accessed May 15th, 2015.
- , , , et al. Inappropriate medication use among frail elderly inpatients. Ann Pharmacother. 2004;38:9–14.
- , , , et al. Inappropriate medications in elderly ICU survivors: where to intervene? Arch Intern Med. 2011;171:1032–1034.
- , , , et al. Appropriateness of medication prescribing in ambulatory elderly patients. J Am Geriatr Soc. 1994;42:1241–1247.
- , , , . Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55:780–791.
- , , , . Shared risk factors for falls, incontinence, and functional dependence. Unifying the approach to geriatric syndromes. JAMA. 1995;273:1348–1353.
- , , , . Geriatric syndromes: medical misnomer or progress in geriatrics? Neth J Med. 2003;61:83–87.
- , , , et al. Geriatric conditions in acutely hospitalized older patients: prevalence and one‐year survival and functional decline. PLoS One. 2011;6:e26951.
- , , , , . Geriatric conditions as predictors of increased number of hospital admissions and hospital bed days over one year: findings of a nationwide cohort of older adults from Taiwan. Arch Gerontol Geriatr. 2014;59:169–174.
- , , , , . Geriatric conditions and disability: the Health and Retirement Study. Ann Intern Med. 2007;147:156–164.
- , , , , , . A prospective cohort study of geriatric syndromes among older medical patients admitted to acute care hospitals. J Am Geriatr Soc. 2011;59:2001–2008.
- , , , et al. Geriatric syndromes in hospitalized older adults discharged to skilled nursing facilities. J Am Geriatr Soc. 2016;64(4):715–722.
- , , , et al. Discharge to a skilled nursing facility and subsequent clinical outcomes among older patients hospitalized for heart failure. Circ Heart Fail. 2011;4:293–300.
- . Hazards of hospitalization of the elderly. Ann Intern Med. 1993;118:219–223.
- , , , , . Geriatric syndromes in elderly patients admitted to an inpatient cardiology ward. J Hosp Med. 2007;2:394–400.
- , , , . Effect of hospitalization on inappropriate prescribing in elderly Medicare beneficiaries. J Am Geriatr Soc. 2015;63:699–707.
- , , , et al. Polypharmacy cutoff and outcomes: five or more medicines were used to identify community‐dwelling older men at risk of different adverse outcomes. J Clin Epidemiol. 2012;65:989–995.
- , , , , . Investigating polypharmacy and drug burden index in hospitalised older people. Intern Med J. 2013;43:912–918.
- , . Potentially harmful drug‐drug interactions in the elderly: a review. Am J Geriatr Pharmacother. 2011;9:364–377.
- , , , . Hospital admissions/visits associated with drug‐drug interactions: a systematic review and meta‐analysis. Pharmacoepidemiol Drug Saf. 2014;23:489–497.
- , . Optimising drug treatment for elderly people: the prescribing cascade. BMJ. 1997;315:1096–1099.
- , , , et al. Association between acute geriatric syndromes and medication‐related hospital admissions. Drugs Aging. 2012;29:691–699.
- American Geriatrics Society Beers Criteria Update Expert P. American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2012;60:616–631.
- By the American Geriatrics Society 2015 Beers Criteria Update Expert Panel. American Geriatrics Society 2015 updated Beers criteria for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2015;63:2227–2246.
- , . STOPP (Screening Tool of Older Persons' potentially inappropriate Prescriptions): application to acutely ill elderly patients and comparison with Beers' criteria. Age Ageing. 2008;37:673–679.
- , , , et al. Warfarin versus aspirin for stroke prevention in an elderly community population with atrial fibrillation (the Birmingham Atrial Fibrillation Treatment of the Aged Study, BAFTA): a randomised controlled trial. Lancet. 2007;370:493–503.
- U.S. Food and Drug Administration. Drugs. Available at: http://www.fda.gov/Drugs/default.htm. Accessed May 15th, 2015.
- , , , et al. Inappropriate medication use among frail elderly inpatients. Ann Pharmacother. 2004;38:9–14.
- , , , et al. Inappropriate medications in elderly ICU survivors: where to intervene? Arch Intern Med. 2011;171:1032–1034.
- , , , et al. Appropriateness of medication prescribing in ambulatory elderly patients. J Am Geriatr Soc. 1994;42:1241–1247.
FDA is investigating Zecuity sumatriptan patch for reports of serious burns
The Food and Drug Administration is evaluating patient reports of serious burns and potential permanent scarring that have occurred with the use of the Zecuity patch (sumatriptan iontophoretic transdermal system) to relieve migraine headaches.
Since September 2015, a large number of patients using the Zecuity patch have reported that they have experienced burns or scars on the skin where the patch was worn. Severe redness, pain, skin discoloration, blistering, and cracked skin were reported, according to the agency’s June 2 drug safety announcement.
The Zecuity patch contains sumatriptan which is a prescription medicine used to treat acute migraine headaches in adults. It is designed to give a dose of medicine by way of a single-use, battery-powered patch that is wrapped around the upper arm or thigh.
“Health care professionals should advise patients who complain of moderate to severe pain at the application site to remove the Zecuity patch immediately,” the safety report states. “Consider a different formulation of sumatriptan or switch these patients to an alternative migraine medicine.”
It is advised that patients should not bathe, shower, or swim while wearing the patch. Patients and health care professionals should report possible side effects involving the Zecuity patch to the FDA MedWatch program.
The Food and Drug Administration is evaluating patient reports of serious burns and potential permanent scarring that have occurred with the use of the Zecuity patch (sumatriptan iontophoretic transdermal system) to relieve migraine headaches.
Since September 2015, a large number of patients using the Zecuity patch have reported that they have experienced burns or scars on the skin where the patch was worn. Severe redness, pain, skin discoloration, blistering, and cracked skin were reported, according to the agency’s June 2 drug safety announcement.
The Zecuity patch contains sumatriptan which is a prescription medicine used to treat acute migraine headaches in adults. It is designed to give a dose of medicine by way of a single-use, battery-powered patch that is wrapped around the upper arm or thigh.
“Health care professionals should advise patients who complain of moderate to severe pain at the application site to remove the Zecuity patch immediately,” the safety report states. “Consider a different formulation of sumatriptan or switch these patients to an alternative migraine medicine.”
It is advised that patients should not bathe, shower, or swim while wearing the patch. Patients and health care professionals should report possible side effects involving the Zecuity patch to the FDA MedWatch program.
The Food and Drug Administration is evaluating patient reports of serious burns and potential permanent scarring that have occurred with the use of the Zecuity patch (sumatriptan iontophoretic transdermal system) to relieve migraine headaches.
Since September 2015, a large number of patients using the Zecuity patch have reported that they have experienced burns or scars on the skin where the patch was worn. Severe redness, pain, skin discoloration, blistering, and cracked skin were reported, according to the agency’s June 2 drug safety announcement.
The Zecuity patch contains sumatriptan which is a prescription medicine used to treat acute migraine headaches in adults. It is designed to give a dose of medicine by way of a single-use, battery-powered patch that is wrapped around the upper arm or thigh.
“Health care professionals should advise patients who complain of moderate to severe pain at the application site to remove the Zecuity patch immediately,” the safety report states. “Consider a different formulation of sumatriptan or switch these patients to an alternative migraine medicine.”
It is advised that patients should not bathe, shower, or swim while wearing the patch. Patients and health care professionals should report possible side effects involving the Zecuity patch to the FDA MedWatch program.
Starting with combination diabetes therapy beats initial monotherapy
ORLANDO – Whether to start a patient with newly diagnosed type 2 diabetes mellitus on combination therapy or monotherapy should be based on experimentation and observation rather than expert opinion, according to Dr. Alan Garber, president of the American College of Endocrinology and professor of medicine, biochemistry, and molecular and cellular biology at Baylor College of Medicine in Houston.
Monotherapy for type 2 diabetes with stepwise addition of other antihyperglycemic agents has long been the accepted way to initiate therapy in this population. Beginning in the 1990s, investigators began to compare the efficacy of monotherapy with combination therapy, first with metformin and glyburide alone or together, and then testing metformin in combination with glipizide, rosiglitazone, and sitagliptin, he said.
For metformin and glyburide, each agent alone lowered glycated hemoglobin (HbA1c), compared with placebo, but adding one to the other enhanced lowering. Combining the two drugs had the greatest benefit for higher HbA1c entry levels (e.g., HbA1c strata of 9%-9.9% or 10% or greater vs. less than 8%). At the highest-entry HbA1c levels, half doses of each of metformin and glyburide (250 mg/1.25 mg, respectively) were more efficacious than full doses of each (500 mg/2.5 mg). “This is called drug sparing,” he said.
In a trial of metformin and rosiglitazone, the combination was superior to either alone, producing significantly greater mean reductions in HbA1c and in fasting plasma glucose (FPG) at 32 weeks from their respective baselines, again, with greater reductions for higher-entry HbA1c levels. The combination was also better than either drug alone in the speed of reducing HbA1c or FPG, and in the final attained levels.
The combination of metformin and a sulfonylurea presents a risk of hypoglycemia, but Dr. Garber said the results are “much cleaner” using combinations of metformin with agents such as a thiazolidinedione, a dipeptidyl peptidase-4 inhibitor, or a sodium/glucose cotransporter-2 inhibitor.
Also noteworthy are findings from the EDICT (Efficacy and Durability of Initial Combination Therapy for Type 2 Diabetes) trial using insulin-sensitizing and insulin-secreting agents metformin/pioglitazone/exenatide in combination vs. escalating doses of metformin with sequential addition of a sulfonylurea and glargine insulin to treat patients with newly diagnosed type 2 diabetes. Over 2 years, the subjects receiving combination therapy had lower HbA1c, a mean weight loss, compared with weight gain, in the sequential therapy group, and a 7.5-fold lower rate of hypoglycemia, compared with the sequential treatment group (Diabetes Obes Metab. 2015;17:268-75).
Although the agents used in the two treatment strategies were not strictly equivalent, “it’s clear that testing multiple therapeutic mechanisms tends to produce better outcomes than fewer therapeutic mechanisms,” Dr. Garber said. The conclusions are fairly straightforward. “Look for evidence to support what strategies you want to use for your patients’ care.”
Using the Kaiser Permanente database, investigators found that the mean time of having an HbA1c above 8% was 3 years before a second agent was added, and the mean HbA1c was 9%. Many people have ascribed this sort of delay to a problem with the physician. But Dr. Garber said it is more related to patients, who often resist prescriptions for more drugs. So starting with two drugs may produce better efficacy faster as well as overcome the psychological issues of trying to add another one later (Am J Manag Care. 2003;9:213-7).
Session moderator Dr. Daniel Einhorn, medical director of the Scripps Whittier Diabetes Institute in La Jolla, California, raised the possibility of “subtraction therapy, where you start with three agents no matter what, and then if things go well, you subtract. And so you reverse the situation that Alan discussed.” In the patient’s view, “you have a celebration that night instead of a wake,” he said.
Dr. Garber has received honoraria or consulting fees as a member of the advisory boards of Novo Nordisk, Janssen, and Merck. Dr. Einhorn is on the scientific advisory boards of Eli Lilly, Novo Nordisk, Janssen, Boehringer Ingelheim, Sanofi, and Adocia, is a consultant for Halozyme, Glysens, Freedom-Meditech, and Epitracker, and has research funding from Lilly, Novo, Janssen, AstraZeneca, Mannkind, Freedom-Meditech, Merck, Sanofi, and Boehringer Ingelheim.
ORLANDO – Whether to start a patient with newly diagnosed type 2 diabetes mellitus on combination therapy or monotherapy should be based on experimentation and observation rather than expert opinion, according to Dr. Alan Garber, president of the American College of Endocrinology and professor of medicine, biochemistry, and molecular and cellular biology at Baylor College of Medicine in Houston.
Monotherapy for type 2 diabetes with stepwise addition of other antihyperglycemic agents has long been the accepted way to initiate therapy in this population. Beginning in the 1990s, investigators began to compare the efficacy of monotherapy with combination therapy, first with metformin and glyburide alone or together, and then testing metformin in combination with glipizide, rosiglitazone, and sitagliptin, he said.
For metformin and glyburide, each agent alone lowered glycated hemoglobin (HbA1c), compared with placebo, but adding one to the other enhanced lowering. Combining the two drugs had the greatest benefit for higher HbA1c entry levels (e.g., HbA1c strata of 9%-9.9% or 10% or greater vs. less than 8%). At the highest-entry HbA1c levels, half doses of each of metformin and glyburide (250 mg/1.25 mg, respectively) were more efficacious than full doses of each (500 mg/2.5 mg). “This is called drug sparing,” he said.
In a trial of metformin and rosiglitazone, the combination was superior to either alone, producing significantly greater mean reductions in HbA1c and in fasting plasma glucose (FPG) at 32 weeks from their respective baselines, again, with greater reductions for higher-entry HbA1c levels. The combination was also better than either drug alone in the speed of reducing HbA1c or FPG, and in the final attained levels.
The combination of metformin and a sulfonylurea presents a risk of hypoglycemia, but Dr. Garber said the results are “much cleaner” using combinations of metformin with agents such as a thiazolidinedione, a dipeptidyl peptidase-4 inhibitor, or a sodium/glucose cotransporter-2 inhibitor.
Also noteworthy are findings from the EDICT (Efficacy and Durability of Initial Combination Therapy for Type 2 Diabetes) trial using insulin-sensitizing and insulin-secreting agents metformin/pioglitazone/exenatide in combination vs. escalating doses of metformin with sequential addition of a sulfonylurea and glargine insulin to treat patients with newly diagnosed type 2 diabetes. Over 2 years, the subjects receiving combination therapy had lower HbA1c, a mean weight loss, compared with weight gain, in the sequential therapy group, and a 7.5-fold lower rate of hypoglycemia, compared with the sequential treatment group (Diabetes Obes Metab. 2015;17:268-75).
Although the agents used in the two treatment strategies were not strictly equivalent, “it’s clear that testing multiple therapeutic mechanisms tends to produce better outcomes than fewer therapeutic mechanisms,” Dr. Garber said. The conclusions are fairly straightforward. “Look for evidence to support what strategies you want to use for your patients’ care.”
Using the Kaiser Permanente database, investigators found that the mean time of having an HbA1c above 8% was 3 years before a second agent was added, and the mean HbA1c was 9%. Many people have ascribed this sort of delay to a problem with the physician. But Dr. Garber said it is more related to patients, who often resist prescriptions for more drugs. So starting with two drugs may produce better efficacy faster as well as overcome the psychological issues of trying to add another one later (Am J Manag Care. 2003;9:213-7).
Session moderator Dr. Daniel Einhorn, medical director of the Scripps Whittier Diabetes Institute in La Jolla, California, raised the possibility of “subtraction therapy, where you start with three agents no matter what, and then if things go well, you subtract. And so you reverse the situation that Alan discussed.” In the patient’s view, “you have a celebration that night instead of a wake,” he said.
Dr. Garber has received honoraria or consulting fees as a member of the advisory boards of Novo Nordisk, Janssen, and Merck. Dr. Einhorn is on the scientific advisory boards of Eli Lilly, Novo Nordisk, Janssen, Boehringer Ingelheim, Sanofi, and Adocia, is a consultant for Halozyme, Glysens, Freedom-Meditech, and Epitracker, and has research funding from Lilly, Novo, Janssen, AstraZeneca, Mannkind, Freedom-Meditech, Merck, Sanofi, and Boehringer Ingelheim.
ORLANDO – Whether to start a patient with newly diagnosed type 2 diabetes mellitus on combination therapy or monotherapy should be based on experimentation and observation rather than expert opinion, according to Dr. Alan Garber, president of the American College of Endocrinology and professor of medicine, biochemistry, and molecular and cellular biology at Baylor College of Medicine in Houston.
Monotherapy for type 2 diabetes with stepwise addition of other antihyperglycemic agents has long been the accepted way to initiate therapy in this population. Beginning in the 1990s, investigators began to compare the efficacy of monotherapy with combination therapy, first with metformin and glyburide alone or together, and then testing metformin in combination with glipizide, rosiglitazone, and sitagliptin, he said.
For metformin and glyburide, each agent alone lowered glycated hemoglobin (HbA1c), compared with placebo, but adding one to the other enhanced lowering. Combining the two drugs had the greatest benefit for higher HbA1c entry levels (e.g., HbA1c strata of 9%-9.9% or 10% or greater vs. less than 8%). At the highest-entry HbA1c levels, half doses of each of metformin and glyburide (250 mg/1.25 mg, respectively) were more efficacious than full doses of each (500 mg/2.5 mg). “This is called drug sparing,” he said.
In a trial of metformin and rosiglitazone, the combination was superior to either alone, producing significantly greater mean reductions in HbA1c and in fasting plasma glucose (FPG) at 32 weeks from their respective baselines, again, with greater reductions for higher-entry HbA1c levels. The combination was also better than either drug alone in the speed of reducing HbA1c or FPG, and in the final attained levels.
The combination of metformin and a sulfonylurea presents a risk of hypoglycemia, but Dr. Garber said the results are “much cleaner” using combinations of metformin with agents such as a thiazolidinedione, a dipeptidyl peptidase-4 inhibitor, or a sodium/glucose cotransporter-2 inhibitor.
Also noteworthy are findings from the EDICT (Efficacy and Durability of Initial Combination Therapy for Type 2 Diabetes) trial using insulin-sensitizing and insulin-secreting agents metformin/pioglitazone/exenatide in combination vs. escalating doses of metformin with sequential addition of a sulfonylurea and glargine insulin to treat patients with newly diagnosed type 2 diabetes. Over 2 years, the subjects receiving combination therapy had lower HbA1c, a mean weight loss, compared with weight gain, in the sequential therapy group, and a 7.5-fold lower rate of hypoglycemia, compared with the sequential treatment group (Diabetes Obes Metab. 2015;17:268-75).
Although the agents used in the two treatment strategies were not strictly equivalent, “it’s clear that testing multiple therapeutic mechanisms tends to produce better outcomes than fewer therapeutic mechanisms,” Dr. Garber said. The conclusions are fairly straightforward. “Look for evidence to support what strategies you want to use for your patients’ care.”
Using the Kaiser Permanente database, investigators found that the mean time of having an HbA1c above 8% was 3 years before a second agent was added, and the mean HbA1c was 9%. Many people have ascribed this sort of delay to a problem with the physician. But Dr. Garber said it is more related to patients, who often resist prescriptions for more drugs. So starting with two drugs may produce better efficacy faster as well as overcome the psychological issues of trying to add another one later (Am J Manag Care. 2003;9:213-7).
Session moderator Dr. Daniel Einhorn, medical director of the Scripps Whittier Diabetes Institute in La Jolla, California, raised the possibility of “subtraction therapy, where you start with three agents no matter what, and then if things go well, you subtract. And so you reverse the situation that Alan discussed.” In the patient’s view, “you have a celebration that night instead of a wake,” he said.
Dr. Garber has received honoraria or consulting fees as a member of the advisory boards of Novo Nordisk, Janssen, and Merck. Dr. Einhorn is on the scientific advisory boards of Eli Lilly, Novo Nordisk, Janssen, Boehringer Ingelheim, Sanofi, and Adocia, is a consultant for Halozyme, Glysens, Freedom-Meditech, and Epitracker, and has research funding from Lilly, Novo, Janssen, AstraZeneca, Mannkind, Freedom-Meditech, Merck, Sanofi, and Boehringer Ingelheim.
EXPERT ANALYSIS AT AACE 2016
RF ablation successfully treats focal adrenal tumors
ORLANDO – Radiofrequency (RF) ablation is a safe and effective procedure for treating focal adrenal tumors in patients who are poor surgical candidates or who refuse adrenalectomy. With a short treatment time and minimal hospital stay, RF ablation can provide rapid clinical and biochemical improvement.
Dr. Lima Lawrence, an internal medicine resident at the University of Illinois at Chicago/Advocate Christ Medical Center in Oak Lawn, presented a case report and a review of the literature during an oral abstract session at the annual meeting of the American Association of Clinical Endocrinologists. The patient was a 65-year-old woman who presented with weight gain, decreased energy, and muscle weakness. On physical exam, she was hypertensive, anxious, obese, and had prominent supraclavicular fat pads. Salivary cortisol and overnight dexamethasone suppression tests were both elevated, and ACTH levels were depressed, confirming the diagnosis of a cortisol-secreting tumor causing adrenal Cushing’s syndrome. Computed tomography (CT) surveillance showed a progressively enlarging right-sided adrenal mass. A peritoneal biopsy revealed a low-grade serous neoplasm of peritoneal origin.
Her medical history included type 2 diabetes, uncontrolled hypertension, mixed connective tissue disease, depression, and total abdominal hysterectomy with bilateral salpingo-oophorectomy for ovarian cancer.
Dr. Lawrence said the patient had been scheduled for adrenalectomy, but it was not performed because of an intraoperative finding of peritoneal studding from what turned out to be metastatic ovarian cancer. Therefore, she underwent CT-guided RF ablation of the adrenal mass using a 14-gauge probe that heated a 3.5-cm ablation zone to 50-60 C for 8-10 minutes to achieve complete tumor necrosis.
The patient showed dramatic “clinical and biochemical improvement,” Dr. Lawrence said. The patient had no procedural complications and no blood loss and was observed for 23 hours before being discharged to home. A CT scan 8 weeks later showed a slightly decreased mass with marked decreased radiographic attenuation post-contrast from 30.2 Hounsfield Units (HU) preoperatively to 17 HU on follow-up.
Potential adverse outcomes using RF ablation include a risk of pneumothorax, hemothorax, and tumor seeding along the catheter track, but this last possibility can be mitigated by continuing to heat the RF probe as it is withdrawn.
Published evidence supports use of RF ablation. “To date there have been no randomized clinical trials comparing the safety, efficacy, and survival benefits of adrenalectomy vs. radio frequency ablation,” she said. It may not be feasible to do a randomized trial. But a review of the literature generally supports the efficacy of the technique although the publications each involved a small series of patients, Dr. Lawrence said in an interview.
A 2003 series (Cancer. 2003;97:554-60) of 15 primary or metastatic adrenal cell carcinomas that were unresectable or were in patients who were not surgical candidates showed nonenhancement and no growth in 8 (53%) at a mean follow-up of 10.3 months. Eight of the 12 tumors of 5 cm or smaller had complete loss of radiographic enhancement and a decrease in size.
From a retrospective series of 13 patients with functional adrenal neoplasms over 7 years, there was 100% resolution of biochemical abnormalities and clinical symptoms at a mean follow-up of 21.2 months. One small pneumothorax and one limited hemothorax occurred, neither of which required hospital admission. There were two instances of transient, self-remitting hypertension associated with the procedures (Radiology. 2011;258:308-16).
In 2015, one group of investigators followed 11 patients for 12 weeks postprocedure. Eight of nine patients with Conn’s syndrome attained normal serum aldosterone levels. One with a nodule close to the inferior vena cava had incomplete ablation. Two of two Cushing’s patients had normal cortisol levels after the procedure (J Vasc Interv Radiol. 2015;26:1459-64).
A retrospective analysis of 16 adrenal metastases showed that 13 (81%) had no local progression over 14 months after ablation. In two of three functional adrenal neoplasms, clinical and biochemical abnormalities resolved (Eur J Radiol. 2012.81:1717-23).
A retrospective series of 10 adrenal metastases showed that one recurred at 7 months after image-guided thermal ablation, with no recurrence of the rest at 26.6 months. There was no tumor recurrence for any of the cases of metastatic disease localized to the RF ablation site (J Vasc Interv Radiol. 2014;25:593-8).
Results were somewhat less good in a retrospective evaluation of 35 patients with unresectable adrenal masses over 9 years. Although 33 of 35 (94%) lost tumor enhancement after the initial adrenal RF ablation, there was local tumor progression in 8 of 35 (23%) patients at a mean follow-up of 30.1 months (Radiology. 2015;277:584-93).
Finally, Dr. Lawrence discussed a systematic literature review on adrenalectomy vs. stereotactic ablative body radiotherapy (SABR) and percutaneous catheter ablation (PCA) in the treatment of adrenal metastases: 30 papers on adrenalectomy on 818 patients; 9 papers on SABR on 178 patients; and 6 papers on PCA, including RF ablation, on 51 patients. The authors concluded that there was “insufficient evidence to determine the best local treatment modality for isolated or limited adrenal metastases.” Adrenalectomy appeared to be a reasonable treatment for suitable patients. SABR was a valid alternative for nonsurgical candidates, but they did not recommend PCA until more long-term outcomes were available (Cancer Treat Rev. 2014;40:838-46).
Dr. Lawrence concurred, based on her case study and literature review. She said RF ablation “offers patients a minimally invasive option for treating focal adrenal tumors” and is a “safe and effective procedure … in patients who are poor surgical candidates or refuse adrenalectomy.” More long-term follow-up studies are needed before RF ablation could replace adrenalectomy, she noted.
ORLANDO – Radiofrequency (RF) ablation is a safe and effective procedure for treating focal adrenal tumors in patients who are poor surgical candidates or who refuse adrenalectomy. With a short treatment time and minimal hospital stay, RF ablation can provide rapid clinical and biochemical improvement.
Dr. Lima Lawrence, an internal medicine resident at the University of Illinois at Chicago/Advocate Christ Medical Center in Oak Lawn, presented a case report and a review of the literature during an oral abstract session at the annual meeting of the American Association of Clinical Endocrinologists. The patient was a 65-year-old woman who presented with weight gain, decreased energy, and muscle weakness. On physical exam, she was hypertensive, anxious, obese, and had prominent supraclavicular fat pads. Salivary cortisol and overnight dexamethasone suppression tests were both elevated, and ACTH levels were depressed, confirming the diagnosis of a cortisol-secreting tumor causing adrenal Cushing’s syndrome. Computed tomography (CT) surveillance showed a progressively enlarging right-sided adrenal mass. A peritoneal biopsy revealed a low-grade serous neoplasm of peritoneal origin.
Her medical history included type 2 diabetes, uncontrolled hypertension, mixed connective tissue disease, depression, and total abdominal hysterectomy with bilateral salpingo-oophorectomy for ovarian cancer.
Dr. Lawrence said the patient had been scheduled for adrenalectomy, but it was not performed because of an intraoperative finding of peritoneal studding from what turned out to be metastatic ovarian cancer. Therefore, she underwent CT-guided RF ablation of the adrenal mass using a 14-gauge probe that heated a 3.5-cm ablation zone to 50-60 C for 8-10 minutes to achieve complete tumor necrosis.
The patient showed dramatic “clinical and biochemical improvement,” Dr. Lawrence said. The patient had no procedural complications and no blood loss and was observed for 23 hours before being discharged to home. A CT scan 8 weeks later showed a slightly decreased mass with marked decreased radiographic attenuation post-contrast from 30.2 Hounsfield Units (HU) preoperatively to 17 HU on follow-up.
Potential adverse outcomes using RF ablation include a risk of pneumothorax, hemothorax, and tumor seeding along the catheter track, but this last possibility can be mitigated by continuing to heat the RF probe as it is withdrawn.
Published evidence supports use of RF ablation. “To date there have been no randomized clinical trials comparing the safety, efficacy, and survival benefits of adrenalectomy vs. radio frequency ablation,” she said. It may not be feasible to do a randomized trial. But a review of the literature generally supports the efficacy of the technique although the publications each involved a small series of patients, Dr. Lawrence said in an interview.
A 2003 series (Cancer. 2003;97:554-60) of 15 primary or metastatic adrenal cell carcinomas that were unresectable or were in patients who were not surgical candidates showed nonenhancement and no growth in 8 (53%) at a mean follow-up of 10.3 months. Eight of the 12 tumors of 5 cm or smaller had complete loss of radiographic enhancement and a decrease in size.
From a retrospective series of 13 patients with functional adrenal neoplasms over 7 years, there was 100% resolution of biochemical abnormalities and clinical symptoms at a mean follow-up of 21.2 months. One small pneumothorax and one limited hemothorax occurred, neither of which required hospital admission. There were two instances of transient, self-remitting hypertension associated with the procedures (Radiology. 2011;258:308-16).
In 2015, one group of investigators followed 11 patients for 12 weeks postprocedure. Eight of nine patients with Conn’s syndrome attained normal serum aldosterone levels. One with a nodule close to the inferior vena cava had incomplete ablation. Two of two Cushing’s patients had normal cortisol levels after the procedure (J Vasc Interv Radiol. 2015;26:1459-64).
A retrospective analysis of 16 adrenal metastases showed that 13 (81%) had no local progression over 14 months after ablation. In two of three functional adrenal neoplasms, clinical and biochemical abnormalities resolved (Eur J Radiol. 2012.81:1717-23).
A retrospective series of 10 adrenal metastases showed that one recurred at 7 months after image-guided thermal ablation, with no recurrence of the rest at 26.6 months. There was no tumor recurrence for any of the cases of metastatic disease localized to the RF ablation site (J Vasc Interv Radiol. 2014;25:593-8).
Results were somewhat less good in a retrospective evaluation of 35 patients with unresectable adrenal masses over 9 years. Although 33 of 35 (94%) lost tumor enhancement after the initial adrenal RF ablation, there was local tumor progression in 8 of 35 (23%) patients at a mean follow-up of 30.1 months (Radiology. 2015;277:584-93).
Finally, Dr. Lawrence discussed a systematic literature review on adrenalectomy vs. stereotactic ablative body radiotherapy (SABR) and percutaneous catheter ablation (PCA) in the treatment of adrenal metastases: 30 papers on adrenalectomy on 818 patients; 9 papers on SABR on 178 patients; and 6 papers on PCA, including RF ablation, on 51 patients. The authors concluded that there was “insufficient evidence to determine the best local treatment modality for isolated or limited adrenal metastases.” Adrenalectomy appeared to be a reasonable treatment for suitable patients. SABR was a valid alternative for nonsurgical candidates, but they did not recommend PCA until more long-term outcomes were available (Cancer Treat Rev. 2014;40:838-46).
Dr. Lawrence concurred, based on her case study and literature review. She said RF ablation “offers patients a minimally invasive option for treating focal adrenal tumors” and is a “safe and effective procedure … in patients who are poor surgical candidates or refuse adrenalectomy.” More long-term follow-up studies are needed before RF ablation could replace adrenalectomy, she noted.
ORLANDO – Radiofrequency (RF) ablation is a safe and effective procedure for treating focal adrenal tumors in patients who are poor surgical candidates or who refuse adrenalectomy. With a short treatment time and minimal hospital stay, RF ablation can provide rapid clinical and biochemical improvement.
Dr. Lima Lawrence, an internal medicine resident at the University of Illinois at Chicago/Advocate Christ Medical Center in Oak Lawn, presented a case report and a review of the literature during an oral abstract session at the annual meeting of the American Association of Clinical Endocrinologists. The patient was a 65-year-old woman who presented with weight gain, decreased energy, and muscle weakness. On physical exam, she was hypertensive, anxious, obese, and had prominent supraclavicular fat pads. Salivary cortisol and overnight dexamethasone suppression tests were both elevated, and ACTH levels were depressed, confirming the diagnosis of a cortisol-secreting tumor causing adrenal Cushing’s syndrome. Computed tomography (CT) surveillance showed a progressively enlarging right-sided adrenal mass. A peritoneal biopsy revealed a low-grade serous neoplasm of peritoneal origin.
Her medical history included type 2 diabetes, uncontrolled hypertension, mixed connective tissue disease, depression, and total abdominal hysterectomy with bilateral salpingo-oophorectomy for ovarian cancer.
Dr. Lawrence said the patient had been scheduled for adrenalectomy, but it was not performed because of an intraoperative finding of peritoneal studding from what turned out to be metastatic ovarian cancer. Therefore, she underwent CT-guided RF ablation of the adrenal mass using a 14-gauge probe that heated a 3.5-cm ablation zone to 50-60 C for 8-10 minutes to achieve complete tumor necrosis.
The patient showed dramatic “clinical and biochemical improvement,” Dr. Lawrence said. The patient had no procedural complications and no blood loss and was observed for 23 hours before being discharged to home. A CT scan 8 weeks later showed a slightly decreased mass with marked decreased radiographic attenuation post-contrast from 30.2 Hounsfield Units (HU) preoperatively to 17 HU on follow-up.
Potential adverse outcomes using RF ablation include a risk of pneumothorax, hemothorax, and tumor seeding along the catheter track, but this last possibility can be mitigated by continuing to heat the RF probe as it is withdrawn.
Published evidence supports use of RF ablation. “To date there have been no randomized clinical trials comparing the safety, efficacy, and survival benefits of adrenalectomy vs. radio frequency ablation,” she said. It may not be feasible to do a randomized trial. But a review of the literature generally supports the efficacy of the technique although the publications each involved a small series of patients, Dr. Lawrence said in an interview.
A 2003 series (Cancer. 2003;97:554-60) of 15 primary or metastatic adrenal cell carcinomas that were unresectable or were in patients who were not surgical candidates showed nonenhancement and no growth in 8 (53%) at a mean follow-up of 10.3 months. Eight of the 12 tumors of 5 cm or smaller had complete loss of radiographic enhancement and a decrease in size.
From a retrospective series of 13 patients with functional adrenal neoplasms over 7 years, there was 100% resolution of biochemical abnormalities and clinical symptoms at a mean follow-up of 21.2 months. One small pneumothorax and one limited hemothorax occurred, neither of which required hospital admission. There were two instances of transient, self-remitting hypertension associated with the procedures (Radiology. 2011;258:308-16).
In 2015, one group of investigators followed 11 patients for 12 weeks postprocedure. Eight of nine patients with Conn’s syndrome attained normal serum aldosterone levels. One with a nodule close to the inferior vena cava had incomplete ablation. Two of two Cushing’s patients had normal cortisol levels after the procedure (J Vasc Interv Radiol. 2015;26:1459-64).
A retrospective analysis of 16 adrenal metastases showed that 13 (81%) had no local progression over 14 months after ablation. In two of three functional adrenal neoplasms, clinical and biochemical abnormalities resolved (Eur J Radiol. 2012.81:1717-23).
A retrospective series of 10 adrenal metastases showed that one recurred at 7 months after image-guided thermal ablation, with no recurrence of the rest at 26.6 months. There was no tumor recurrence for any of the cases of metastatic disease localized to the RF ablation site (J Vasc Interv Radiol. 2014;25:593-8).
Results were somewhat less good in a retrospective evaluation of 35 patients with unresectable adrenal masses over 9 years. Although 33 of 35 (94%) lost tumor enhancement after the initial adrenal RF ablation, there was local tumor progression in 8 of 35 (23%) patients at a mean follow-up of 30.1 months (Radiology. 2015;277:584-93).
Finally, Dr. Lawrence discussed a systematic literature review on adrenalectomy vs. stereotactic ablative body radiotherapy (SABR) and percutaneous catheter ablation (PCA) in the treatment of adrenal metastases: 30 papers on adrenalectomy on 818 patients; 9 papers on SABR on 178 patients; and 6 papers on PCA, including RF ablation, on 51 patients. The authors concluded that there was “insufficient evidence to determine the best local treatment modality for isolated or limited adrenal metastases.” Adrenalectomy appeared to be a reasonable treatment for suitable patients. SABR was a valid alternative for nonsurgical candidates, but they did not recommend PCA until more long-term outcomes were available (Cancer Treat Rev. 2014;40:838-46).
Dr. Lawrence concurred, based on her case study and literature review. She said RF ablation “offers patients a minimally invasive option for treating focal adrenal tumors” and is a “safe and effective procedure … in patients who are poor surgical candidates or refuse adrenalectomy.” More long-term follow-up studies are needed before RF ablation could replace adrenalectomy, she noted.
EXPERT ANALYSIS AT AACE 2016
Give serious thought before starting antipsychotics in elders
The page filled me with dread: “Your elderly patient is confused, getting out of bed, and threw her entire dinner tray at a nurse just now.” Because this morning my patient was polite and appropriate, the now-angry, dinner-splattered nurse means only one thing: delirium.
Delirium is one of the most difficult problems for hospitalists to manage, in part because our management of delirium is often learned on the fly during residency and early years of practice. This post-hoc approach toward delirium misses the most important aspect of treatment: Prevention.
Interventions like early mobilization, environmental interventions, careful oversight of drugs, hydration protocols, and reinforcing the day/night cycle are crucial. Unfortunately, few hospitals can provide these resources or the trained multidisciplinary team with geriatrics expertise to administer them. The result is that delirium occurs more frequently than it should, and hospitalists often face a patient who is a risk to themselves or others.
In this situation, antipsychotics (APs) are often prescribed. However, long term use of APs by elders is highly discouraged by many organizations, including the Society of Hospital Medicine, because of risks like cardiac events (e.g., QT prolongation), stroke, extrapyramidal symptoms, falls, somnolence, and increased mortality in older patients with dementia.
One unanswered question regarding the use of APs is whether starting the medications in the hospital results in long-term use. To answer this, we performed a retrospective study of 300 elderly hospitalized patients who were treated for the first time with APs during their hospitalization. Of these 300 patients, 10% died during that first hospitalization, and almost half (48%) remained on APs at discharge. We found that most of the prescriptions were to treat delirium (J Hosp Med. 2014 Dec;9[12]:802-4. doi:10.1002/jhm.2277).
In a more recent study, we looked at outcomes for patients discharged from Baystate Medical Center (Springfield, Mass.) on APs. Within a year of discharge, 40% of these patients were readmitted at least once and approximately two-thirds were still taking the same APs on which they had been discharged (J Hosp Med. 2016 Apr 6. doi: 10.1002/jhm.2585). Thus, if I start my patient described above (who threw the dinner tray) on an AP today, she is very likely to be readmitted the next year still taking that same medication. Starting an AP in the short term can lead to the very thing we have been warned against: long-term use of an AP in an elderly person.
Even more striking than the continuation rate was the incredibly high 1-year mortality rate. Of the 260 patients discharged from the original admission on an AP, one-third had died at the 1-year mark. This group of patients had a wide range of diagnoses, but nearly as many died as if they all had stage IV heart failure. Because most had an agitated delirium at the time of AP prescription, these findings suggest that onset of in-hospital delirium should trigger a closer examination of the patient’s current burden of illness, prognosis, functional and cognitive status, treatment options, and goals of care.
Prevention is key
Our study also supports the prevention of delirium as the most important strategy to improve patient outcomes. Since conducting this study, Baystate Medical Center has implemented an “ACE” (Acute Care for Elders) pilot project and will soon open a full ACE unit. This unit, which employs many of the behavioral interventions described above (early discharge planning, drug oversight, team-based care, early mobilization, optimizing vision and hearing, sleep-wake cycle preservation, and hydration) has resulted in declines in both delirium rates and use of APs. Use of restraints has been virtually eliminated.
Our ACE program was a combined effort between geriatrics, hospital medicine, nursing, pharmacy, and others, but hospitalists often lead acute care quality improvement (QI) initiatives, and are superbly positioned to champion programs like ACE, NICHE (Nurses Improving Care for Health System Elders), and HELP (Hospital Elder Life Program) to benefit this vulnerable population.
Some important questions about AP use remain unanswered. First, there is very limited clinical trial evidence that APs actually improve outcomes in patients with delirium. Second, our study fails to answer one all-important question: does long-term AP use increase mortality in elders? Prior studies are largely retrospective, and results have been mixed.
Our study highlights the difficulty of teasing apart the baseline risks of the patients, the risk of the medications themselves, and confounding variables. There may be an association between APs and death, but it is quite possible that patients who require APs are simply at higher risk of death independent of the drugs’ effect; this confounding by indication cannot be adjusted away.
This leaves hospitalists in a difficult position. At Baystate Medical Center, hospitalists have opted to focus on prevention, but when delirium occurs, some patients are still treated with APs. Clinicians reserve the medications for patients who are suffering and fail to respond to nonpharmacologic interventions or are a risk to themselves or others. Still, Baystate plans to reduce use even in this population by instituting behavioral response teams to devise nondrug care plans, and hospitalists are encouraged to avoid discharging patients on APs.
Finally, even though patients who require APs may lack a clear terminal diagnosis, we encourage clinicians to recognize that delirium should prompt a discussion of prognosis and clarification of values, goals, and realistic treatment options.
Dr. Loh is a fellow at the James Wilmot Cancer Center, University of Rochester (N.Y.) Medical Center. Dr. Brennan is chief of geriatrics and post-acute medicine at Baystate Medical Center, Springfield, Mass. Dr. Lagu is an academic hospitalist in the Center for Quality of Care Research at Baystate Medical Center.
The page filled me with dread: “Your elderly patient is confused, getting out of bed, and threw her entire dinner tray at a nurse just now.” Because this morning my patient was polite and appropriate, the now-angry, dinner-splattered nurse means only one thing: delirium.
Delirium is one of the most difficult problems for hospitalists to manage, in part because our management of delirium is often learned on the fly during residency and early years of practice. This post-hoc approach toward delirium misses the most important aspect of treatment: Prevention.
Interventions like early mobilization, environmental interventions, careful oversight of drugs, hydration protocols, and reinforcing the day/night cycle are crucial. Unfortunately, few hospitals can provide these resources or the trained multidisciplinary team with geriatrics expertise to administer them. The result is that delirium occurs more frequently than it should, and hospitalists often face a patient who is a risk to themselves or others.
In this situation, antipsychotics (APs) are often prescribed. However, long term use of APs by elders is highly discouraged by many organizations, including the Society of Hospital Medicine, because of risks like cardiac events (e.g., QT prolongation), stroke, extrapyramidal symptoms, falls, somnolence, and increased mortality in older patients with dementia.
One unanswered question regarding the use of APs is whether starting the medications in the hospital results in long-term use. To answer this, we performed a retrospective study of 300 elderly hospitalized patients who were treated for the first time with APs during their hospitalization. Of these 300 patients, 10% died during that first hospitalization, and almost half (48%) remained on APs at discharge. We found that most of the prescriptions were to treat delirium (J Hosp Med. 2014 Dec;9[12]:802-4. doi:10.1002/jhm.2277).
In a more recent study, we looked at outcomes for patients discharged from Baystate Medical Center (Springfield, Mass.) on APs. Within a year of discharge, 40% of these patients were readmitted at least once and approximately two-thirds were still taking the same APs on which they had been discharged (J Hosp Med. 2016 Apr 6. doi: 10.1002/jhm.2585). Thus, if I start my patient described above (who threw the dinner tray) on an AP today, she is very likely to be readmitted the next year still taking that same medication. Starting an AP in the short term can lead to the very thing we have been warned against: long-term use of an AP in an elderly person.
Even more striking than the continuation rate was the incredibly high 1-year mortality rate. Of the 260 patients discharged from the original admission on an AP, one-third had died at the 1-year mark. This group of patients had a wide range of diagnoses, but nearly as many died as if they all had stage IV heart failure. Because most had an agitated delirium at the time of AP prescription, these findings suggest that onset of in-hospital delirium should trigger a closer examination of the patient’s current burden of illness, prognosis, functional and cognitive status, treatment options, and goals of care.
Prevention is key
Our study also supports the prevention of delirium as the most important strategy to improve patient outcomes. Since conducting this study, Baystate Medical Center has implemented an “ACE” (Acute Care for Elders) pilot project and will soon open a full ACE unit. This unit, which employs many of the behavioral interventions described above (early discharge planning, drug oversight, team-based care, early mobilization, optimizing vision and hearing, sleep-wake cycle preservation, and hydration) has resulted in declines in both delirium rates and use of APs. Use of restraints has been virtually eliminated.
Our ACE program was a combined effort between geriatrics, hospital medicine, nursing, pharmacy, and others, but hospitalists often lead acute care quality improvement (QI) initiatives, and are superbly positioned to champion programs like ACE, NICHE (Nurses Improving Care for Health System Elders), and HELP (Hospital Elder Life Program) to benefit this vulnerable population.
Some important questions about AP use remain unanswered. First, there is very limited clinical trial evidence that APs actually improve outcomes in patients with delirium. Second, our study fails to answer one all-important question: does long-term AP use increase mortality in elders? Prior studies are largely retrospective, and results have been mixed.
Our study highlights the difficulty of teasing apart the baseline risks of the patients, the risk of the medications themselves, and confounding variables. There may be an association between APs and death, but it is quite possible that patients who require APs are simply at higher risk of death independent of the drugs’ effect; this confounding by indication cannot be adjusted away.
This leaves hospitalists in a difficult position. At Baystate Medical Center, hospitalists have opted to focus on prevention, but when delirium occurs, some patients are still treated with APs. Clinicians reserve the medications for patients who are suffering and fail to respond to nonpharmacologic interventions or are a risk to themselves or others. Still, Baystate plans to reduce use even in this population by instituting behavioral response teams to devise nondrug care plans, and hospitalists are encouraged to avoid discharging patients on APs.
Finally, even though patients who require APs may lack a clear terminal diagnosis, we encourage clinicians to recognize that delirium should prompt a discussion of prognosis and clarification of values, goals, and realistic treatment options.
Dr. Loh is a fellow at the James Wilmot Cancer Center, University of Rochester (N.Y.) Medical Center. Dr. Brennan is chief of geriatrics and post-acute medicine at Baystate Medical Center, Springfield, Mass. Dr. Lagu is an academic hospitalist in the Center for Quality of Care Research at Baystate Medical Center.
The page filled me with dread: “Your elderly patient is confused, getting out of bed, and threw her entire dinner tray at a nurse just now.” Because this morning my patient was polite and appropriate, the now-angry, dinner-splattered nurse means only one thing: delirium.
Delirium is one of the most difficult problems for hospitalists to manage, in part because our management of delirium is often learned on the fly during residency and early years of practice. This post-hoc approach toward delirium misses the most important aspect of treatment: Prevention.
Interventions like early mobilization, environmental interventions, careful oversight of drugs, hydration protocols, and reinforcing the day/night cycle are crucial. Unfortunately, few hospitals can provide these resources or the trained multidisciplinary team with geriatrics expertise to administer them. The result is that delirium occurs more frequently than it should, and hospitalists often face a patient who is a risk to themselves or others.
In this situation, antipsychotics (APs) are often prescribed. However, long term use of APs by elders is highly discouraged by many organizations, including the Society of Hospital Medicine, because of risks like cardiac events (e.g., QT prolongation), stroke, extrapyramidal symptoms, falls, somnolence, and increased mortality in older patients with dementia.
One unanswered question regarding the use of APs is whether starting the medications in the hospital results in long-term use. To answer this, we performed a retrospective study of 300 elderly hospitalized patients who were treated for the first time with APs during their hospitalization. Of these 300 patients, 10% died during that first hospitalization, and almost half (48%) remained on APs at discharge. We found that most of the prescriptions were to treat delirium (J Hosp Med. 2014 Dec;9[12]:802-4. doi:10.1002/jhm.2277).
In a more recent study, we looked at outcomes for patients discharged from Baystate Medical Center (Springfield, Mass.) on APs. Within a year of discharge, 40% of these patients were readmitted at least once and approximately two-thirds were still taking the same APs on which they had been discharged (J Hosp Med. 2016 Apr 6. doi: 10.1002/jhm.2585). Thus, if I start my patient described above (who threw the dinner tray) on an AP today, she is very likely to be readmitted the next year still taking that same medication. Starting an AP in the short term can lead to the very thing we have been warned against: long-term use of an AP in an elderly person.
Even more striking than the continuation rate was the incredibly high 1-year mortality rate. Of the 260 patients discharged from the original admission on an AP, one-third had died at the 1-year mark. This group of patients had a wide range of diagnoses, but nearly as many died as if they all had stage IV heart failure. Because most had an agitated delirium at the time of AP prescription, these findings suggest that onset of in-hospital delirium should trigger a closer examination of the patient’s current burden of illness, prognosis, functional and cognitive status, treatment options, and goals of care.
Prevention is key
Our study also supports the prevention of delirium as the most important strategy to improve patient outcomes. Since conducting this study, Baystate Medical Center has implemented an “ACE” (Acute Care for Elders) pilot project and will soon open a full ACE unit. This unit, which employs many of the behavioral interventions described above (early discharge planning, drug oversight, team-based care, early mobilization, optimizing vision and hearing, sleep-wake cycle preservation, and hydration) has resulted in declines in both delirium rates and use of APs. Use of restraints has been virtually eliminated.
Our ACE program was a combined effort between geriatrics, hospital medicine, nursing, pharmacy, and others, but hospitalists often lead acute care quality improvement (QI) initiatives, and are superbly positioned to champion programs like ACE, NICHE (Nurses Improving Care for Health System Elders), and HELP (Hospital Elder Life Program) to benefit this vulnerable population.
Some important questions about AP use remain unanswered. First, there is very limited clinical trial evidence that APs actually improve outcomes in patients with delirium. Second, our study fails to answer one all-important question: does long-term AP use increase mortality in elders? Prior studies are largely retrospective, and results have been mixed.
Our study highlights the difficulty of teasing apart the baseline risks of the patients, the risk of the medications themselves, and confounding variables. There may be an association between APs and death, but it is quite possible that patients who require APs are simply at higher risk of death independent of the drugs’ effect; this confounding by indication cannot be adjusted away.
This leaves hospitalists in a difficult position. At Baystate Medical Center, hospitalists have opted to focus on prevention, but when delirium occurs, some patients are still treated with APs. Clinicians reserve the medications for patients who are suffering and fail to respond to nonpharmacologic interventions or are a risk to themselves or others. Still, Baystate plans to reduce use even in this population by instituting behavioral response teams to devise nondrug care plans, and hospitalists are encouraged to avoid discharging patients on APs.
Finally, even though patients who require APs may lack a clear terminal diagnosis, we encourage clinicians to recognize that delirium should prompt a discussion of prognosis and clarification of values, goals, and realistic treatment options.
Dr. Loh is a fellow at the James Wilmot Cancer Center, University of Rochester (N.Y.) Medical Center. Dr. Brennan is chief of geriatrics and post-acute medicine at Baystate Medical Center, Springfield, Mass. Dr. Lagu is an academic hospitalist in the Center for Quality of Care Research at Baystate Medical Center.
Low-FODMAP diet eased abdominal symptoms in IBS
SAN DIEGO – For patients with diarrhea-predominant irritable bowel syndrome, avoiding FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) improved abdominal pain and bloating significantly more than following standard advice to eat smaller meals and limit caffeine and alcohol, researchers reported.
“Both diets provided adequate relief to about 40%-50% of patients, but the low-FODMAP diet led to significantly greater improvements in abdominal symptoms,” Dr. Shanti L. Eswaran of the University of Michigan, Ann Arbor, said at the annual Digestive Disease Week. Results from the randomized, controlled trial, the first of its kind in the United States, “support a role for the low-FODMAP diet in the treatment of patients with diarrhea-predominant IBS,” she added.
FODMAPs are poorly absorbed or indigestible fermentable carbohydrates that can cause bloating, flatulence, and diarrhea when eaten in excess. Hence, the low-FODMAP diet involves avoiding or limiting foods high in fructose (such as honey and dried fruit), lactose (dairy), fructans (wheat, garlic, and onions), galactans (legumes), and polyols (apples and stone fruits). Several smaller studies have linked a low-FODMAP diet to improvements in IBS, “but the existing data are limited and inconsistent, and there is no randomized, controlled trial data from adults in the United States,” Dr. Eswaran said.
To fill that gap, she and her associates randomly assigned 92 adults meeting Rome III criteria for diarrhea-predominant IBS to follow either a low-FODMAP diet or a control diet that was based on recommendations from the National Institute for Health Care and Excellence (NICE, in the United Kingdom). The modified NICE diet included eating smaller, more frequent meals, limiting caffeine and alcohol, and avoiding foods that patients knew worsened their symptoms. Both groups of patients worked with a dietitian.
At baseline, all patients reported having regular bouts of at least moderate abdominal pain and stool consistency of 5 or higher (that is, looser) on the Bristol Stool Form Scale. In all, 52% of patients on the low-FODMAP diet and 41% of patients on the control diet reported adequate symptom relief during at least one of the last 2 weeks of the study – a statistically similar level of improvement, Dr. Eswaran said. “We were really underpowered for our primary endpoint,” she added. “We had calculated a 30% difference, and we did not get anywhere near that.” In fact, enrollment in the trial ended early because many patients were already putting themselves on the low-FODMAP diet, she added.
But despite its limited power, the study uncovered significant differences in abdominal symptoms with the two diets. More than half of patients on the low-FODMAP diet reported a clinically meaningful improvement in abdominal pain, compared with only 23% of patients on the control diet (P = .008). Likewise, 52% of patients reported clinically meaningful improvement in bloating, compared with about a quarter of patients on the control diet (P = .013). Low-FODMAP patients also were more likely to report improvements in stool consistency (42%, versus 28% for control patients; P = .18). However, there was no evidence that the low-FODMAP diet improved stool consistency or urgency, Dr. Eswaran said.
“Both diets were safe and well tolerated, although dropouts were more common with the low-FODMAP diet,” the researchers noted. Dietary analyses showed that at 4 weeks, the low-FODMAP group was consuming significantly less total carbohydrates, but similar quantities of total calories, protein, fat, dietary fiber, and alcohol as the control group. “The low-FODMAP diet is not designed to be long term, because it is fairly restrictive,” Dr. Eswaran commented. “I think it would be a good idea for the next set of studies to see how long patients can stay on it, and what factors are necessary for them to do so.”
Dr. Eswaran had no disclosures.
SAN DIEGO – For patients with diarrhea-predominant irritable bowel syndrome, avoiding FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) improved abdominal pain and bloating significantly more than following standard advice to eat smaller meals and limit caffeine and alcohol, researchers reported.
“Both diets provided adequate relief to about 40%-50% of patients, but the low-FODMAP diet led to significantly greater improvements in abdominal symptoms,” Dr. Shanti L. Eswaran of the University of Michigan, Ann Arbor, said at the annual Digestive Disease Week. Results from the randomized, controlled trial, the first of its kind in the United States, “support a role for the low-FODMAP diet in the treatment of patients with diarrhea-predominant IBS,” she added.
FODMAPs are poorly absorbed or indigestible fermentable carbohydrates that can cause bloating, flatulence, and diarrhea when eaten in excess. Hence, the low-FODMAP diet involves avoiding or limiting foods high in fructose (such as honey and dried fruit), lactose (dairy), fructans (wheat, garlic, and onions), galactans (legumes), and polyols (apples and stone fruits). Several smaller studies have linked a low-FODMAP diet to improvements in IBS, “but the existing data are limited and inconsistent, and there is no randomized, controlled trial data from adults in the United States,” Dr. Eswaran said.
To fill that gap, she and her associates randomly assigned 92 adults meeting Rome III criteria for diarrhea-predominant IBS to follow either a low-FODMAP diet or a control diet that was based on recommendations from the National Institute for Health Care and Excellence (NICE, in the United Kingdom). The modified NICE diet included eating smaller, more frequent meals, limiting caffeine and alcohol, and avoiding foods that patients knew worsened their symptoms. Both groups of patients worked with a dietitian.
At baseline, all patients reported having regular bouts of at least moderate abdominal pain and stool consistency of 5 or higher (that is, looser) on the Bristol Stool Form Scale. In all, 52% of patients on the low-FODMAP diet and 41% of patients on the control diet reported adequate symptom relief during at least one of the last 2 weeks of the study – a statistically similar level of improvement, Dr. Eswaran said. “We were really underpowered for our primary endpoint,” she added. “We had calculated a 30% difference, and we did not get anywhere near that.” In fact, enrollment in the trial ended early because many patients were already putting themselves on the low-FODMAP diet, she added.
But despite its limited power, the study uncovered significant differences in abdominal symptoms with the two diets. More than half of patients on the low-FODMAP diet reported a clinically meaningful improvement in abdominal pain, compared with only 23% of patients on the control diet (P = .008). Likewise, 52% of patients reported clinically meaningful improvement in bloating, compared with about a quarter of patients on the control diet (P = .013). Low-FODMAP patients also were more likely to report improvements in stool consistency (42%, versus 28% for control patients; P = .18). However, there was no evidence that the low-FODMAP diet improved stool consistency or urgency, Dr. Eswaran said.
“Both diets were safe and well tolerated, although dropouts were more common with the low-FODMAP diet,” the researchers noted. Dietary analyses showed that at 4 weeks, the low-FODMAP group was consuming significantly less total carbohydrates, but similar quantities of total calories, protein, fat, dietary fiber, and alcohol as the control group. “The low-FODMAP diet is not designed to be long term, because it is fairly restrictive,” Dr. Eswaran commented. “I think it would be a good idea for the next set of studies to see how long patients can stay on it, and what factors are necessary for them to do so.”
Dr. Eswaran had no disclosures.
SAN DIEGO – For patients with diarrhea-predominant irritable bowel syndrome, avoiding FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) improved abdominal pain and bloating significantly more than following standard advice to eat smaller meals and limit caffeine and alcohol, researchers reported.
“Both diets provided adequate relief to about 40%-50% of patients, but the low-FODMAP diet led to significantly greater improvements in abdominal symptoms,” Dr. Shanti L. Eswaran of the University of Michigan, Ann Arbor, said at the annual Digestive Disease Week. Results from the randomized, controlled trial, the first of its kind in the United States, “support a role for the low-FODMAP diet in the treatment of patients with diarrhea-predominant IBS,” she added.
FODMAPs are poorly absorbed or indigestible fermentable carbohydrates that can cause bloating, flatulence, and diarrhea when eaten in excess. Hence, the low-FODMAP diet involves avoiding or limiting foods high in fructose (such as honey and dried fruit), lactose (dairy), fructans (wheat, garlic, and onions), galactans (legumes), and polyols (apples and stone fruits). Several smaller studies have linked a low-FODMAP diet to improvements in IBS, “but the existing data are limited and inconsistent, and there is no randomized, controlled trial data from adults in the United States,” Dr. Eswaran said.
To fill that gap, she and her associates randomly assigned 92 adults meeting Rome III criteria for diarrhea-predominant IBS to follow either a low-FODMAP diet or a control diet that was based on recommendations from the National Institute for Health Care and Excellence (NICE, in the United Kingdom). The modified NICE diet included eating smaller, more frequent meals, limiting caffeine and alcohol, and avoiding foods that patients knew worsened their symptoms. Both groups of patients worked with a dietitian.
At baseline, all patients reported having regular bouts of at least moderate abdominal pain and stool consistency of 5 or higher (that is, looser) on the Bristol Stool Form Scale. In all, 52% of patients on the low-FODMAP diet and 41% of patients on the control diet reported adequate symptom relief during at least one of the last 2 weeks of the study – a statistically similar level of improvement, Dr. Eswaran said. “We were really underpowered for our primary endpoint,” she added. “We had calculated a 30% difference, and we did not get anywhere near that.” In fact, enrollment in the trial ended early because many patients were already putting themselves on the low-FODMAP diet, she added.
But despite its limited power, the study uncovered significant differences in abdominal symptoms with the two diets. More than half of patients on the low-FODMAP diet reported a clinically meaningful improvement in abdominal pain, compared with only 23% of patients on the control diet (P = .008). Likewise, 52% of patients reported clinically meaningful improvement in bloating, compared with about a quarter of patients on the control diet (P = .013). Low-FODMAP patients also were more likely to report improvements in stool consistency (42%, versus 28% for control patients; P = .18). However, there was no evidence that the low-FODMAP diet improved stool consistency or urgency, Dr. Eswaran said.
“Both diets were safe and well tolerated, although dropouts were more common with the low-FODMAP diet,” the researchers noted. Dietary analyses showed that at 4 weeks, the low-FODMAP group was consuming significantly less total carbohydrates, but similar quantities of total calories, protein, fat, dietary fiber, and alcohol as the control group. “The low-FODMAP diet is not designed to be long term, because it is fairly restrictive,” Dr. Eswaran commented. “I think it would be a good idea for the next set of studies to see how long patients can stay on it, and what factors are necessary for them to do so.”
Dr. Eswaran had no disclosures.
AT DDW® 2016
Key clinical point: A diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols was associated with significant and clinically meaningful improvements in the abdominal symptoms of diarrhea-predominant irritable bowel syndrome.
Major finding: About half of patients on the low-FODMAP diet improved, compared with about a quarter of patients on a common-sense control diet.
Data source: A prospective, single-center, single-blind randomized controlled trial of 92 adults with IBS with diarrhea (Rome III).
Disclosures: Dr. Eswaren had no disclosures.
Firm Gray Nodule on the Scalp
The Diagnosis: Primary Cutaneous Mucinous Carcinoma
Primary cutaneous mucinous carcinoma is a rare tumor of the sweat glands that was first reported in 1952 by Lennox et al.1 These tumors are slow growing and have a predilection for the head and neck, with the eyelid being the most commonly reported location.2 In general, they present as erythematous asymptomatic nodules measuring less than 7 cm in diameter.2-4 Primary cutaneous mucinous carcinoma tends to have a good prognosis with complete resection, but cases of metastasis and recurrence have been reported.2 Although there is no standard of care, treatment typically consists of surgical management, as the tumors are nonresponsive to chemotherapy or radiation.4 Kamalpour et al2 compared outcomes for Mohs micrographic surgery versus standard excision, the former showing a lower percentage of poor outcomes. Of note, there were fewer cases treated with Mohs surgery in this study; only more recently reported cases have been treated with Mohs surgery.
Histologically, primary cutaneous mucinous carcinoma is composed of cords, tubules, and lobules of epithelial cells floating in large pools of basophilic mucin, separated by thin fibrovascular septa.5 It can be difficult to distinguish a primary tumor from a mucinous carcinoma metastasis with histology alone, especially on the breasts and in the gastrointestinal tract. Immunohistochemistry can be helpful in determining the origin of the tumor. A homologue of p53, p63 expressed in basal and myoepithelial cells of the skin can aid in the confirmation of a primary tumor when present.6,7 Negative staining for cytokeratin 20 and positive staining for cytokeratin 7 also are helpful in distinguishing a primary cutaneous mucinous carcinoma from a gastrointestinal tract metastasis.4,8
In our patient, no other symptoms were present that raised concern for an internal malignancy. Findings that supported a primary versus metastatic tumor included the clinicopathologic findings (Figure) as well as positive p63, cytokeratin 7, and negative cytokeratin 20 staining. The initial standard excision had tumor cells within 1 mm of the specimen margin; thus, a subsequent wider reexcision was performed. Reexcision was negative for tumor cells. Close follow-up with a primary care physician was recommended, with emphasis on colon and breast cancer screening. A follow-up mammogram was negative for breast cancer.

- Lennox B, Pearse AG, Richards HG. Mucin-secreting tumours of the skin: with special reference to the so-called mixed-salivary tumour of the skin and its relation to hidradenoma. J Pathol Bacteriol. 1952;64:865-880.
- Kamalpour L, Brindise RT, Nodzenski M, et al. Primary cutaneous mucinous carcinoma a systematic review and meta-analysis of outcomes after surgery. JAMA Dermatol. 2014;150:380-384.
- Papalas JA, Proia AD. Primary mucinous carcinoma of the eyelid: a clinicopathological and immunohistochemical study of 4 cases and an update on recurrence rates. Arch Ophthalmol. 2010;128:1160-1165.
- Breiting L, Christensen L, Dahlstrom K, et al. Primary mucinous carcinoma of the skin: a population-based study. Int J Dermatol. 2008;47:242-245.
- Walsh SN, Santa Cruz DJ. Adnexal carcinomas of the skin. In: Rigel DS, Robinson JK, Ross M, et al, eds. Cancer of the Skin. 2nd ed. Beijing, China: Elsevier Saunders; 2011:140-149.
- Jo VY, Fletcher CD. p63 Immunohistochemical staining is limited in soft tissue tumors. Am J Clin Pathol. 2011;136:762-766.
- Ivan D, Nash JW, Prieto VG, et al. Use of p63 expression in distinguishing primary and metastatic cutaneous adnexal neoplasms from metastatic adenocarcinoma to skin. J Cutan Pathol. 2006;34:478-489.
- Kazakov DV, Suster S, LeBoit PE, et al. Mucinous carcinoma of the skin, primary, and secondary: a clinicopathologic study of 63 cases with emphasis on the morphologic spectrum of primary cutaneous forms: homologies with mucinous lesions in the breast. Am J Surg Pathol. 2005;29:764-782.
The Diagnosis: Primary Cutaneous Mucinous Carcinoma
Primary cutaneous mucinous carcinoma is a rare tumor of the sweat glands that was first reported in 1952 by Lennox et al.1 These tumors are slow growing and have a predilection for the head and neck, with the eyelid being the most commonly reported location.2 In general, they present as erythematous asymptomatic nodules measuring less than 7 cm in diameter.2-4 Primary cutaneous mucinous carcinoma tends to have a good prognosis with complete resection, but cases of metastasis and recurrence have been reported.2 Although there is no standard of care, treatment typically consists of surgical management, as the tumors are nonresponsive to chemotherapy or radiation.4 Kamalpour et al2 compared outcomes for Mohs micrographic surgery versus standard excision, the former showing a lower percentage of poor outcomes. Of note, there were fewer cases treated with Mohs surgery in this study; only more recently reported cases have been treated with Mohs surgery.
Histologically, primary cutaneous mucinous carcinoma is composed of cords, tubules, and lobules of epithelial cells floating in large pools of basophilic mucin, separated by thin fibrovascular septa.5 It can be difficult to distinguish a primary tumor from a mucinous carcinoma metastasis with histology alone, especially on the breasts and in the gastrointestinal tract. Immunohistochemistry can be helpful in determining the origin of the tumor. A homologue of p53, p63 expressed in basal and myoepithelial cells of the skin can aid in the confirmation of a primary tumor when present.6,7 Negative staining for cytokeratin 20 and positive staining for cytokeratin 7 also are helpful in distinguishing a primary cutaneous mucinous carcinoma from a gastrointestinal tract metastasis.4,8
In our patient, no other symptoms were present that raised concern for an internal malignancy. Findings that supported a primary versus metastatic tumor included the clinicopathologic findings (Figure) as well as positive p63, cytokeratin 7, and negative cytokeratin 20 staining. The initial standard excision had tumor cells within 1 mm of the specimen margin; thus, a subsequent wider reexcision was performed. Reexcision was negative for tumor cells. Close follow-up with a primary care physician was recommended, with emphasis on colon and breast cancer screening. A follow-up mammogram was negative for breast cancer.

The Diagnosis: Primary Cutaneous Mucinous Carcinoma
Primary cutaneous mucinous carcinoma is a rare tumor of the sweat glands that was first reported in 1952 by Lennox et al.1 These tumors are slow growing and have a predilection for the head and neck, with the eyelid being the most commonly reported location.2 In general, they present as erythematous asymptomatic nodules measuring less than 7 cm in diameter.2-4 Primary cutaneous mucinous carcinoma tends to have a good prognosis with complete resection, but cases of metastasis and recurrence have been reported.2 Although there is no standard of care, treatment typically consists of surgical management, as the tumors are nonresponsive to chemotherapy or radiation.4 Kamalpour et al2 compared outcomes for Mohs micrographic surgery versus standard excision, the former showing a lower percentage of poor outcomes. Of note, there were fewer cases treated with Mohs surgery in this study; only more recently reported cases have been treated with Mohs surgery.
Histologically, primary cutaneous mucinous carcinoma is composed of cords, tubules, and lobules of epithelial cells floating in large pools of basophilic mucin, separated by thin fibrovascular septa.5 It can be difficult to distinguish a primary tumor from a mucinous carcinoma metastasis with histology alone, especially on the breasts and in the gastrointestinal tract. Immunohistochemistry can be helpful in determining the origin of the tumor. A homologue of p53, p63 expressed in basal and myoepithelial cells of the skin can aid in the confirmation of a primary tumor when present.6,7 Negative staining for cytokeratin 20 and positive staining for cytokeratin 7 also are helpful in distinguishing a primary cutaneous mucinous carcinoma from a gastrointestinal tract metastasis.4,8
In our patient, no other symptoms were present that raised concern for an internal malignancy. Findings that supported a primary versus metastatic tumor included the clinicopathologic findings (Figure) as well as positive p63, cytokeratin 7, and negative cytokeratin 20 staining. The initial standard excision had tumor cells within 1 mm of the specimen margin; thus, a subsequent wider reexcision was performed. Reexcision was negative for tumor cells. Close follow-up with a primary care physician was recommended, with emphasis on colon and breast cancer screening. A follow-up mammogram was negative for breast cancer.

- Lennox B, Pearse AG, Richards HG. Mucin-secreting tumours of the skin: with special reference to the so-called mixed-salivary tumour of the skin and its relation to hidradenoma. J Pathol Bacteriol. 1952;64:865-880.
- Kamalpour L, Brindise RT, Nodzenski M, et al. Primary cutaneous mucinous carcinoma a systematic review and meta-analysis of outcomes after surgery. JAMA Dermatol. 2014;150:380-384.
- Papalas JA, Proia AD. Primary mucinous carcinoma of the eyelid: a clinicopathological and immunohistochemical study of 4 cases and an update on recurrence rates. Arch Ophthalmol. 2010;128:1160-1165.
- Breiting L, Christensen L, Dahlstrom K, et al. Primary mucinous carcinoma of the skin: a population-based study. Int J Dermatol. 2008;47:242-245.
- Walsh SN, Santa Cruz DJ. Adnexal carcinomas of the skin. In: Rigel DS, Robinson JK, Ross M, et al, eds. Cancer of the Skin. 2nd ed. Beijing, China: Elsevier Saunders; 2011:140-149.
- Jo VY, Fletcher CD. p63 Immunohistochemical staining is limited in soft tissue tumors. Am J Clin Pathol. 2011;136:762-766.
- Ivan D, Nash JW, Prieto VG, et al. Use of p63 expression in distinguishing primary and metastatic cutaneous adnexal neoplasms from metastatic adenocarcinoma to skin. J Cutan Pathol. 2006;34:478-489.
- Kazakov DV, Suster S, LeBoit PE, et al. Mucinous carcinoma of the skin, primary, and secondary: a clinicopathologic study of 63 cases with emphasis on the morphologic spectrum of primary cutaneous forms: homologies with mucinous lesions in the breast. Am J Surg Pathol. 2005;29:764-782.
- Lennox B, Pearse AG, Richards HG. Mucin-secreting tumours of the skin: with special reference to the so-called mixed-salivary tumour of the skin and its relation to hidradenoma. J Pathol Bacteriol. 1952;64:865-880.
- Kamalpour L, Brindise RT, Nodzenski M, et al. Primary cutaneous mucinous carcinoma a systematic review and meta-analysis of outcomes after surgery. JAMA Dermatol. 2014;150:380-384.
- Papalas JA, Proia AD. Primary mucinous carcinoma of the eyelid: a clinicopathological and immunohistochemical study of 4 cases and an update on recurrence rates. Arch Ophthalmol. 2010;128:1160-1165.
- Breiting L, Christensen L, Dahlstrom K, et al. Primary mucinous carcinoma of the skin: a population-based study. Int J Dermatol. 2008;47:242-245.
- Walsh SN, Santa Cruz DJ. Adnexal carcinomas of the skin. In: Rigel DS, Robinson JK, Ross M, et al, eds. Cancer of the Skin. 2nd ed. Beijing, China: Elsevier Saunders; 2011:140-149.
- Jo VY, Fletcher CD. p63 Immunohistochemical staining is limited in soft tissue tumors. Am J Clin Pathol. 2011;136:762-766.
- Ivan D, Nash JW, Prieto VG, et al. Use of p63 expression in distinguishing primary and metastatic cutaneous adnexal neoplasms from metastatic adenocarcinoma to skin. J Cutan Pathol. 2006;34:478-489.
- Kazakov DV, Suster S, LeBoit PE, et al. Mucinous carcinoma of the skin, primary, and secondary: a clinicopathologic study of 63 cases with emphasis on the morphologic spectrum of primary cutaneous forms: homologies with mucinous lesions in the breast. Am J Surg Pathol. 2005;29:764-782.

Dimethyl Fumarate and Fingolimod May Decrease Relapse Rate More Than Other DMTs
NATIONAL HARBOR, MD—In a real-world comparison in patients with multiple sclerosis (MS), dimethyl fumarate and fingolimod were associated with the largest reduction in unadjusted relapse rates after initiation of disease-modifying therapy (DMT), according to data presented at the 2016 CMSC Annual Meeting. In addition, dimethyl fumarate was associated with significantly fewer arrhythmias, compared with glatiramer acetate, interferon beta, and teriflunomide after initiation of DMT.
Real-world data on the comparative effectiveness of DMTs for MS management are limited. The goal of this study, led by Aaron Boster, MD, Systems Medical Chief of Neuroimmunology for OhioHealth in Columbus, Ohio, and his colleagues was to compare the annual relapse rate in patients initiating delayed-release dimethyl fumarate, glatiramer acetate, interferon beta, fingolimod, or teriflunomide.
For this investigation, researchers used data from the Truven MarketScan Claim database, which includes information from 80 million commercially insured people in the United States. Patients with MS between ages 18 and 64 who initiated a DMT of choice in 2013 were included in the study.
Aaron Boster, MD
Dr. Boster and his colleagues calculated arrhythmias based on the number of MS-related relapses within one year after DMT initiation and examined chronic disease burden and MS-related symptoms. Composite scores depended on the presence of 22 chronic conditions, including diabetes, peptic ulcer, liver disease, and cancer. The Poisson regression model was used to estimate adjusted incidence rate ratios of relapse rate. The researchers adjusted the data for demographic and clinical characteristics such as age, sex, region, and place of residence.
The most significant decreases in unadjusted relapse rate were among patients receiving dimethyl fumarate or fingolimod. Dimethyl fumarate was associated with a lower number of arrhythmias, compared with other DMTs. Overall, patients initiating dimethyl fumarate or fingolimod were more adherent to treatment than patients receiving teriflunomide, glatiramer acetate, or interferon beta in the first year after DMT initiation. “Insights provided by real-world data, and the implications for differences in real-world comparative effectiveness of available DMTs, should be taken into account when making decisions on appropriate therapy for the management of MS,” said Dr. Boster and colleagues.
Some limitations of the study were that the data were not collected specifically for clinical research and that the results that did not provide certain clinical information required to assess disease severity properly.
—Erica Robinson
NATIONAL HARBOR, MD—In a real-world comparison in patients with multiple sclerosis (MS), dimethyl fumarate and fingolimod were associated with the largest reduction in unadjusted relapse rates after initiation of disease-modifying therapy (DMT), according to data presented at the 2016 CMSC Annual Meeting. In addition, dimethyl fumarate was associated with significantly fewer arrhythmias, compared with glatiramer acetate, interferon beta, and teriflunomide after initiation of DMT.
Real-world data on the comparative effectiveness of DMTs for MS management are limited. The goal of this study, led by Aaron Boster, MD, Systems Medical Chief of Neuroimmunology for OhioHealth in Columbus, Ohio, and his colleagues was to compare the annual relapse rate in patients initiating delayed-release dimethyl fumarate, glatiramer acetate, interferon beta, fingolimod, or teriflunomide.
For this investigation, researchers used data from the Truven MarketScan Claim database, which includes information from 80 million commercially insured people in the United States. Patients with MS between ages 18 and 64 who initiated a DMT of choice in 2013 were included in the study.
Aaron Boster, MD
Dr. Boster and his colleagues calculated arrhythmias based on the number of MS-related relapses within one year after DMT initiation and examined chronic disease burden and MS-related symptoms. Composite scores depended on the presence of 22 chronic conditions, including diabetes, peptic ulcer, liver disease, and cancer. The Poisson regression model was used to estimate adjusted incidence rate ratios of relapse rate. The researchers adjusted the data for demographic and clinical characteristics such as age, sex, region, and place of residence.
The most significant decreases in unadjusted relapse rate were among patients receiving dimethyl fumarate or fingolimod. Dimethyl fumarate was associated with a lower number of arrhythmias, compared with other DMTs. Overall, patients initiating dimethyl fumarate or fingolimod were more adherent to treatment than patients receiving teriflunomide, glatiramer acetate, or interferon beta in the first year after DMT initiation. “Insights provided by real-world data, and the implications for differences in real-world comparative effectiveness of available DMTs, should be taken into account when making decisions on appropriate therapy for the management of MS,” said Dr. Boster and colleagues.
Some limitations of the study were that the data were not collected specifically for clinical research and that the results that did not provide certain clinical information required to assess disease severity properly.
—Erica Robinson
NATIONAL HARBOR, MD—In a real-world comparison in patients with multiple sclerosis (MS), dimethyl fumarate and fingolimod were associated with the largest reduction in unadjusted relapse rates after initiation of disease-modifying therapy (DMT), according to data presented at the 2016 CMSC Annual Meeting. In addition, dimethyl fumarate was associated with significantly fewer arrhythmias, compared with glatiramer acetate, interferon beta, and teriflunomide after initiation of DMT.
Real-world data on the comparative effectiveness of DMTs for MS management are limited. The goal of this study, led by Aaron Boster, MD, Systems Medical Chief of Neuroimmunology for OhioHealth in Columbus, Ohio, and his colleagues was to compare the annual relapse rate in patients initiating delayed-release dimethyl fumarate, glatiramer acetate, interferon beta, fingolimod, or teriflunomide.
For this investigation, researchers used data from the Truven MarketScan Claim database, which includes information from 80 million commercially insured people in the United States. Patients with MS between ages 18 and 64 who initiated a DMT of choice in 2013 were included in the study.
Aaron Boster, MD
Dr. Boster and his colleagues calculated arrhythmias based on the number of MS-related relapses within one year after DMT initiation and examined chronic disease burden and MS-related symptoms. Composite scores depended on the presence of 22 chronic conditions, including diabetes, peptic ulcer, liver disease, and cancer. The Poisson regression model was used to estimate adjusted incidence rate ratios of relapse rate. The researchers adjusted the data for demographic and clinical characteristics such as age, sex, region, and place of residence.
The most significant decreases in unadjusted relapse rate were among patients receiving dimethyl fumarate or fingolimod. Dimethyl fumarate was associated with a lower number of arrhythmias, compared with other DMTs. Overall, patients initiating dimethyl fumarate or fingolimod were more adherent to treatment than patients receiving teriflunomide, glatiramer acetate, or interferon beta in the first year after DMT initiation. “Insights provided by real-world data, and the implications for differences in real-world comparative effectiveness of available DMTs, should be taken into account when making decisions on appropriate therapy for the management of MS,” said Dr. Boster and colleagues.
Some limitations of the study were that the data were not collected specifically for clinical research and that the results that did not provide certain clinical information required to assess disease severity properly.
—Erica Robinson
Alemtuzumab-Associated Improvement Is Sustained for More Than Five Years
NATIONAL HARBOR, MD—Patients with highly active relapsing-remitting multiple sclerosis (RRMS) have durable improvement for more than five years with alemtuzumab use, according to data presented at the 2016 CMSC Annual Meeting. In a phase III trial, 45% of patients treated with alemtuzumab also sustained a six-month reduction in disability.
Barry A. Singer, MD
In the CARE-MS II study, alemtuzumab was associated with more significant improvement in clinical and MRI outcomes over two years, compared with subcutaneous interferon beta-1a, in patients with active RRMS who had had a poor response to prior therapy at baseline. An extension study was initiated to evaluate the five-year efficacy of alemtuzumab treatment in a subset of patients with RRMS and highly active disease at baseline. Barry A. Singer, MD, Assistant Professor of Clinical Neurology at Washington University in St. Louis, and his colleagues defined highly active disease as two or more relapses in the year before randomization and gadolinium-enhanced lesions at baseline. In the study, 24% of patients receiving alemtuzumab met the criteria for highly active disease.
During the trial, patients randomized to alemtuzumab (12 mg/day) were given two courses of treatment for five consecutive days at baseline and treatment for three consecutive days in the 12th month. In the extension study, patients were only allowed to receive alemtuzumab retreatment if they had a relapse or MRI activity on disease-modifying treatment.
In more than five years, at least 80% of patients receiving alemtuzumab who had had an inadequate response to prior therapy were free of relapses in each individual year. Sixty-two percent of patients had no alemtuzumab retreatment or other disease-modifying therapies. Results also showed that 97% of patients did not receive another disease-modifying therapy.
In this cohort, no evidence of disease activity was reported in 71%, 63%, and 67% of patients during years three, four, and five, as well as in 53% of patients during years zero to five. Arrhythmias remained low in each individual year of the extension study. Expanded Disability Status Scale (EDSS) scores also showed improvement with alemtuzumab use through years zero to five. In addition, sustained reduction in disability was achieved by 53% of patients during years zero to five.
“Based on these findings, alemtuzumab may provide a unique treatment approach with durable efficacy in the absence of continuous treatment for patients with highly active RRMS,” said Dr. Singer and his colleagues.
This study was supported by Genzyme and Bayer HealthCare Pharmaceuticals.
—Erica Robinson
NATIONAL HARBOR, MD—Patients with highly active relapsing-remitting multiple sclerosis (RRMS) have durable improvement for more than five years with alemtuzumab use, according to data presented at the 2016 CMSC Annual Meeting. In a phase III trial, 45% of patients treated with alemtuzumab also sustained a six-month reduction in disability.
Barry A. Singer, MD
In the CARE-MS II study, alemtuzumab was associated with more significant improvement in clinical and MRI outcomes over two years, compared with subcutaneous interferon beta-1a, in patients with active RRMS who had had a poor response to prior therapy at baseline. An extension study was initiated to evaluate the five-year efficacy of alemtuzumab treatment in a subset of patients with RRMS and highly active disease at baseline. Barry A. Singer, MD, Assistant Professor of Clinical Neurology at Washington University in St. Louis, and his colleagues defined highly active disease as two or more relapses in the year before randomization and gadolinium-enhanced lesions at baseline. In the study, 24% of patients receiving alemtuzumab met the criteria for highly active disease.
During the trial, patients randomized to alemtuzumab (12 mg/day) were given two courses of treatment for five consecutive days at baseline and treatment for three consecutive days in the 12th month. In the extension study, patients were only allowed to receive alemtuzumab retreatment if they had a relapse or MRI activity on disease-modifying treatment.
In more than five years, at least 80% of patients receiving alemtuzumab who had had an inadequate response to prior therapy were free of relapses in each individual year. Sixty-two percent of patients had no alemtuzumab retreatment or other disease-modifying therapies. Results also showed that 97% of patients did not receive another disease-modifying therapy.
In this cohort, no evidence of disease activity was reported in 71%, 63%, and 67% of patients during years three, four, and five, as well as in 53% of patients during years zero to five. Arrhythmias remained low in each individual year of the extension study. Expanded Disability Status Scale (EDSS) scores also showed improvement with alemtuzumab use through years zero to five. In addition, sustained reduction in disability was achieved by 53% of patients during years zero to five.
“Based on these findings, alemtuzumab may provide a unique treatment approach with durable efficacy in the absence of continuous treatment for patients with highly active RRMS,” said Dr. Singer and his colleagues.
This study was supported by Genzyme and Bayer HealthCare Pharmaceuticals.
—Erica Robinson
NATIONAL HARBOR, MD—Patients with highly active relapsing-remitting multiple sclerosis (RRMS) have durable improvement for more than five years with alemtuzumab use, according to data presented at the 2016 CMSC Annual Meeting. In a phase III trial, 45% of patients treated with alemtuzumab also sustained a six-month reduction in disability.
Barry A. Singer, MD
In the CARE-MS II study, alemtuzumab was associated with more significant improvement in clinical and MRI outcomes over two years, compared with subcutaneous interferon beta-1a, in patients with active RRMS who had had a poor response to prior therapy at baseline. An extension study was initiated to evaluate the five-year efficacy of alemtuzumab treatment in a subset of patients with RRMS and highly active disease at baseline. Barry A. Singer, MD, Assistant Professor of Clinical Neurology at Washington University in St. Louis, and his colleagues defined highly active disease as two or more relapses in the year before randomization and gadolinium-enhanced lesions at baseline. In the study, 24% of patients receiving alemtuzumab met the criteria for highly active disease.
During the trial, patients randomized to alemtuzumab (12 mg/day) were given two courses of treatment for five consecutive days at baseline and treatment for three consecutive days in the 12th month. In the extension study, patients were only allowed to receive alemtuzumab retreatment if they had a relapse or MRI activity on disease-modifying treatment.
In more than five years, at least 80% of patients receiving alemtuzumab who had had an inadequate response to prior therapy were free of relapses in each individual year. Sixty-two percent of patients had no alemtuzumab retreatment or other disease-modifying therapies. Results also showed that 97% of patients did not receive another disease-modifying therapy.
In this cohort, no evidence of disease activity was reported in 71%, 63%, and 67% of patients during years three, four, and five, as well as in 53% of patients during years zero to five. Arrhythmias remained low in each individual year of the extension study. Expanded Disability Status Scale (EDSS) scores also showed improvement with alemtuzumab use through years zero to five. In addition, sustained reduction in disability was achieved by 53% of patients during years zero to five.
“Based on these findings, alemtuzumab may provide a unique treatment approach with durable efficacy in the absence of continuous treatment for patients with highly active RRMS,” said Dr. Singer and his colleagues.
This study was supported by Genzyme and Bayer HealthCare Pharmaceuticals.
—Erica Robinson



