Affiliations
Division of General Medicine and Geriatrics, Department of Medicine, Baystate Medical Center, Springfield, Massachusetts
Tufts University School of Medicine, Boston, Massachusetts
Given name(s)
Maura J.
Family name
Brennan
Degrees
MD

The Association of Frailty with Discharge Disposition for Hospitalized Community Dwelling Elderly Patients

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Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.1 Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.1 Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

METHODS

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

RESULTS

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

DISCUSSION

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.4 Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.7 Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

 

 

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

 

Files
References

1. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1-15. PubMed
2. Allen LA, Hernandez AF, Peterson ED, et al. Discharge to a skilled nursing facility and subsequent clinical outcomes among older patients hospitalized for heart failure. Circ Heart Fail. 2011;4(3):293-300. PubMed
3. Hakkarainen TW, Arbabi S, Willis M, et al. Outcomes of patients discharged to skilled nursing facilities after acute care hospitalizations. Ann Surg. 2016;263(2):280-285. PubMed
4. Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. PubMed
5. Joseph B, Pandit V, Rhee Petal, et al. Predicting hospital discharge disposition in geriatric trauma patients: is frailty the answer? J Trauma Acute Care Surg. 2014;76(1):196-200. PubMed
6. Romanowski KS, Barsun, A, Pamlieri TL, Greenhalgh DG, Sen S. Frailty score on admission predicts outcomes in elderly burn injury. J Burn Care Res. 2015;36(1):1-6. PubMed
7. Liu SK, Montgomery J, Yan Y, et al. Association between hospital admission risk profile score and skilled nursing or acute rehabilitation facility discharges in hospitalized older adults. J Am Geriatr Soc. 2016;64(10):2095-2100. PubMed

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Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.1 Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.1 Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

METHODS

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

RESULTS

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

DISCUSSION

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.4 Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.7 Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

 

 

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

 

Frailty is a common geriatric syndrome characterized by decreased physiological reserves leading to increased vulnerability to stressors.1 Frail individuals are at increased risk of adverse health outcomes including falls, disability, hospitalization, and mortality.1 Discharge to skilled nursing facilities (SNFs) is also associated with adverse outcomes,2,3 but limited data exist on the utility of frailty in predicting discharge location in medical elders. We aimed to evaluate the association of frailty assessed by the Reported Edmonton Frailty Scale (REFS) with discharge disposition in hospitalized medical patients who were previously living in the community.

METHODS

We conducted a prospective study of community dwelling elders (≥65 years) hospitalized to the medical service from January 2014 to April 2016. Trained research assistants interviewed patients and/or caregivers on hospital day 1; the REFS was used to screen for frailty and the Mini-Cog assessment for cognitive impairment (supplementary Appendixes 1 and 2). The primary outcome was discharge disposition categorized as discharge to home (with or without home health services) or discharge to a postacute care (PAC) facility (SNF or inpatient rehabilitation). Multivariable Poisson regression analysis was used to estimate the relative risk of discharge to a PAC facility. Frailty was grouped into the following 3 categories: (1) not frail, (2) apparently vulnerable/mildly frail, and (3) moderately/severely frail.

RESULTS

Among the 775 patients screened, 272 declined to participate, were non-English speakers, were transferred from another facility, were admitted under observation status, had advanced dementia, or died during hospitalization. Five hundred and three medical patients were included: median age was 80 years (interquartile range 75-86 years); 54.1% were female and 82.9% were white. The most common comorbidities were hypertension (51.7%), diabetes (26.0%), and renal failure (26.0%). Of the included patients, 11.1% had a known diagnosis of dementia and 52.1% screened positive for cognitive impairment (Table).

Overall, 24.9% were not frail, 49.5% were apparently vulnerable/mildly frail, and 25.6% were moderately/severely frail. About two-thirds (64.8%) returned home (40.0% with home healthcare) and 35% were discharged to a PAC facility (97.1% of them to SNF). Compared with patients who were discharged home, those discharged to a PAC facility were older (≥85 years; 26.7% vs 40.1%) and more likely to have dementia (7.7% vs 17.5%) and be frail (apparently vulnerable/mild frailty = 48.5% vs 51.4%%, moderate/severe frailty = 19.9% vs 36.2%; P < .001). Median length of hospital stay was shorter in those returning home (4 vs 5 days, P < .001).

In the multivariate analysis, which was adjusted for demographics, comorbidities, and principal diagnosis, frailty was strongly associated with discharge to PAC facility (apparently vulnerable/mild frailty vs no frailty, relative ratio [RR] = 2.00; 95% confidence interval [CI], 1.28-3.27, and moderate/severe frailty vs no frailty; RR = 2.66, 95% CI, 1.67-4.43). When the frailty score was included as a continuous variable, 1 unit increase in the score was associated with a 12% higher risk for discharge to a PAC facility (RR = 1.12; 95% CI, 1.07-1.17).

DISCUSSION

In this analysis of over 500 community-dwelling elderly medical patients hospitalized at one large tertiary center, we found that almost half of the patients were frail and over one-third had a new discharge to a PAC facility. Frailty, as assessed by REFS, was strongly associated with discharge to a PAC facility after adjusting for possible confounders.

Frailty is increasingly recognized as a useful tool to risk stratify the highly heterogeneous population of elderly people.4 Previous studies reported that frailty was predictive of discharge to PAC facilities in geriatric trauma and burn injury patients.5,6 We found similar results in a population of elderly medical patients. A recent study showed that the Hospital Admission Risk Profile score comprising of age, modified Mini-Mental State Examination (MMSE), and functionality prior to admission was associated with discharge disposition in elderly patients admitted to a single geriatric unit in a rural hospital.7 Our study supports this finding by using a validated measure of frailty, the RFS, and does not include the lengthy MMSE.

Our study has several limitations. First, it a single-center study and results may not be generalizable; however, we included a large sample of patients with a variety of medical diagnoses. Second, the REFS is self-reported posing the risks of recall, respondent bias, and interview bias. We chose the REFS to assess frailty due to its practicality and ease of administration but also its completeness of assessing multiple important geriatric domains. Lastly, we did not collect the reason for discharge to PAC and it may have been a potential confounder.

In conclusion, our study demonstrates that frailty assessed by a practical validated scale, the REFS, is a strong predictor of a new discharge to PAC facilities in older medical patients. Accurate identification of elders at risk for discharge to PAC facilities provides the potential to counsel patients and families and plan for complex post discharge needs. Future studies should identify potential interventions targeting frail patients in which PAC is not obligatory, aiming to increase their chance of being discharged home.

 

 

Disclosure

Drs. Stefan and Ramdass had full access to all the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Stefan, Starr, Brennan, and Ramdass conceived the study. Ms. Liu and Dr. Pekow analyzed the data. Dr. Ramdass prepared the manuscript. Drs. Stefan, Brennan, Lindenauer, and Starr critically reviewed the manuscript for important intellectual content. A subset of the patients included in this study was part of a Health Resources and Services Administration funded Geri-Pal Transformation through Learning and Collaboration project awarded to Baystate Medical Center, grant number U1QHP28702 (PI: Maura J. Brennan). The investigators retained full independence in the conduct of this research. The authors have no conflicts of interest.

 

References

1. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1-15. PubMed
2. Allen LA, Hernandez AF, Peterson ED, et al. Discharge to a skilled nursing facility and subsequent clinical outcomes among older patients hospitalized for heart failure. Circ Heart Fail. 2011;4(3):293-300. PubMed
3. Hakkarainen TW, Arbabi S, Willis M, et al. Outcomes of patients discharged to skilled nursing facilities after acute care hospitalizations. Ann Surg. 2016;263(2):280-285. PubMed
4. Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. PubMed
5. Joseph B, Pandit V, Rhee Petal, et al. Predicting hospital discharge disposition in geriatric trauma patients: is frailty the answer? J Trauma Acute Care Surg. 2014;76(1):196-200. PubMed
6. Romanowski KS, Barsun, A, Pamlieri TL, Greenhalgh DG, Sen S. Frailty score on admission predicts outcomes in elderly burn injury. J Burn Care Res. 2015;36(1):1-6. PubMed
7. Liu SK, Montgomery J, Yan Y, et al. Association between hospital admission risk profile score and skilled nursing or acute rehabilitation facility discharges in hospitalized older adults. J Am Geriatr Soc. 2016;64(10):2095-2100. PubMed

References

1. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1-15. PubMed
2. Allen LA, Hernandez AF, Peterson ED, et al. Discharge to a skilled nursing facility and subsequent clinical outcomes among older patients hospitalized for heart failure. Circ Heart Fail. 2011;4(3):293-300. PubMed
3. Hakkarainen TW, Arbabi S, Willis M, et al. Outcomes of patients discharged to skilled nursing facilities after acute care hospitalizations. Ann Surg. 2016;263(2):280-285. PubMed
4. Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. PubMed
5. Joseph B, Pandit V, Rhee Petal, et al. Predicting hospital discharge disposition in geriatric trauma patients: is frailty the answer? J Trauma Acute Care Surg. 2014;76(1):196-200. PubMed
6. Romanowski KS, Barsun, A, Pamlieri TL, Greenhalgh DG, Sen S. Frailty score on admission predicts outcomes in elderly burn injury. J Burn Care Res. 2015;36(1):1-6. PubMed
7. Liu SK, Montgomery J, Yan Y, et al. Association between hospital admission risk profile score and skilled nursing or acute rehabilitation facility discharges in hospitalized older adults. J Am Geriatr Soc. 2016;64(10):2095-2100. PubMed

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Sheryl K. Ramdass, MD, BMedSci, Department of Geriatrics, Baystate Medical Center, 759 Chestnut Street, Springfield, MA, 01199; Telephone: 413-629-8377; Fax #: 413-794-4054; E-mail: sherylkramdass@gmail.com
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Long‐term Antipsychotics in Elders

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Long‐term outcomes of elders discharged on antipsychotics

Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1
Methodology for defining delirium during chart review. Abbreviations: CAM, Confusion Assessment Method.

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and 2 for discrete factors). All factors with a significance level <0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Demographic Data of the Patients Who Were Discharged on APs and Were Readmitted the First Time Within One Year From Discharge and Circumstances Surrounding APs Initiation During Readmission
Variables Value*
  • NOTE: Abbreviations: APs, antipsychotics; ECG, electrocardiogram; QTc, QT interval; SNF, skilled nursing facility. *N = 60; patients who were discharged on APs during index admission and were readmitted within 1 year from discharge (only first readmission was included). Denominator = 31; number of patients whom APs from index admission were continued during readmission. Denominator = 18; number of patients who were started on APs during readmission. Denominator = 17; number of patients ECG was performed prior to APs administration; ∥Denominator = 4; number of patients ECG was performed after APs administration. Denominator = 55; number of patients who were alive during readmission

Age, mean (range), yr 81.3 (65.599.7)
Gender, no. (%)
Male 36 (60)
Female 24 (40)
Admitted from, no. (%)
Home 33 (55)
Rehabilitation facilities 5 (8)
SNF 22 (37)
Services, no. (%)
Medicine 48 (80)
Surgery 12 (20)
Types of APs continued on readmission (from index admission), no. (%)
Quetiapine 19 (61)
Olanzapine 6 (19)
Risperidone 4 (13)
Haloperidol 2 (7)
Types of APs started during readmission, no. (%)
Quetiapine 7 (39)
Risperidone 2 (11)
Haloperidol 16 (89)
Indications for AP use, no. (%)
Delirium 14 (77)
Undocumented 3 (17)
Other 1 (6)
ECG, no. (%)
Prior to APs administration 17 (94)
After APs administration 4 (22)
QTc prolongation >500 ms, no. (%)
Prior to APs administration 3 (18)
After APs administration∥ 2 (50)
Discharge destination, no. (%)
Home 23 (38)
Rehabilitation facilities 4 (7)
SNF 28 (47)
Death 5 (8)
Figure 2
Flowchart showing the outcomes of patients who were discharged on antipsychotics during index hospitalization and readmitted within 1 year from discharge. Abbreviations: APs, antipsychotics.

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

Multivariable Analysis of One‐Year Mortality From Index Admission in Patients Who Were Started on Antipsychotics.
Odds Ratio 95% Confidence Interval P Value
  • NOTE: Abbreviations: AP, antipsychotic; QTc, QT interval. *For subset of patients for whom electrocardiogram was done.

Age 1.03 0.991.06 0.13
Male sex 0.87 0.501.52 0.63
Risperdal 3.53 0.6419.40 0.15
QTc prolongation after AP administration* 3.41 1.348.67 0.01
Presence of geriatric psychiatry consult 0.30 0.091.04 0.06
Discharged to postacute facilities vs home 2.28 1.104.73 0.03

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.

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References
  1. Inouye SK, Westendorp RGJ, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911922.
  2. Fong TG, Jones RN, Marcantonio ER, et al. Adverse outcomes after hospitalization and delirium in persons with Alzheimer disease. Ann Intern Med. 2012;156:848856, W296.
  3. American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults. American Geriatrics Society abstracted clinical practice guideline for postoperative delirium in older adults. J Am Geriatr Soc. 2015;63:142150.
  4. Practice guideline for the treatment of patients with delirium. American Psychiatric Association. Am J Psychiatry. 1999;156:120.
  5. Potter J, George J; Guideline Development Group. The prevention, diagnosis and management of delirium in older people: concise guidelines. Clin Med (Lond). 2006;6:303308.
  6. Seitz DP, Gill SS, Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:1121.
  7. Maust DT, Kim HM, Seyfried LS, et al. Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. JAMA Psychiatry. 2015;72:438445.
  8. Andrade C, Radhakrishnan R. Safety and efficacy of antipsychotic drugs for the behavioral and psychological symptoms of dementia. Indian J Psychiatry. 2009;51(suppl 1):S87S92.
  9. Gareri P, Fazio P, Manfredi VGL, Sarro G. Use and safety of antipsychotics in behavioral disorders in elderly people with dementia. J Clin Psychopharmacol. 2014;34:109123.
  10. Loh KP, Ramdass S, Garb JL, Brennan MJ, Lindenauer PK, Lagu T. From hospital to community: use of antipsychotics in hospitalized elders. J Hosp Med. 2014;9:802804.
  11. Sesso HD, Paffenbarger RS, Lee I‐M. Comparison of National Death Index and World Wide Web Death Searches. Am J Epidemiol. 2000;152:107111.
  12. Cox DR. Analysis of Binary Data. London, United Kingdom: Methuen; 1970:7699.
  13. Lee ET. Statistical Methods for Survival Data Analysis. New York, NY: John Wiley 1992:233236.
  14. Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014;43:127132.
  15. Grover S, Ghormode D, Ghosh A, et al. Risk factors for delirium and inpatient mortality with delirium. J Postgrad Med. 2013;59:263270.
  16. Avelino‐Silva TJ, Farfel JM, Curiati JAE, Amaral JRG, Campora F, Jacob‐Filho W. Comprehensive geriatric assessment predicts mortality and adverse outcomes in hospitalized older adults. BMC Geriatr. 2014;14:129.
  17. Tsai M‐C, Weng H‐H, Chou S‐Y, Tsai C‐S, Hung T‐H, Su J‐A. One‐year mortality of elderly inpatients with delirium, dementia, or depression seen by a consultation‐liaison service. Psychosomatics. 2012;53:433438.
  18. Hemert AM, Mast RC, Hengeveld MW, Vorstenbosch M. Excess mortality in general hospital patients with delirium: a 5‐year follow‐up of 519 patients seen in psychiatric consultation. J Psychosom Res. 1994;38:339346.
  19. McAvay GJ, Ness PH, Bogardus ST, et al. Older adults discharged from the hospital with delirium: 1‐year outcomes. J Am Geriatr Soc. 2006;54:12451250.
  20. Ballard C, Hanney ML, Theodoulou M, et al. The dementia antipsychotic withdrawal trial (DART‐AD): long‐term follow‐up of a randomised placebo‐controlled trial. Lancet Neurol. 2009;8:151157.
  21. Gill SS, Bronskill SE, Normand S‐LT, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775786.
  22. Wang PS, Schneeweiss S, Avorn J, et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:23352341.
  23. Lopez OL, Becker JT, Chang Y‐F, et al. The long‐term effects of conventional and atypical antipsychotics in patients with probable Alzheimer's disease. Am J Psychiatry. 2013;170:10511058.
  24. Hshieh TT, Yue J, Oh E, et al. Effectiveness of multicomponent nonpharmacological delirium interventions: a meta‐analysis. JAMA Intern Med. 2015;175:512520.
  25. Rubin FH, Neal K, Fenlon K, Hassan S, Inouye SK. Sustainability and scalability of the hospital elder life program at a community hospital. J Am Geriatr Soc. 2011;59:359365.
  26. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60:22372245.
  27. Muench J, Hamer AM. Adverse effects of antipsychotic medications. Am Fam Physician. 2010;81:617622.
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Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1
Methodology for defining delirium during chart review. Abbreviations: CAM, Confusion Assessment Method.

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and 2 for discrete factors). All factors with a significance level <0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Demographic Data of the Patients Who Were Discharged on APs and Were Readmitted the First Time Within One Year From Discharge and Circumstances Surrounding APs Initiation During Readmission
Variables Value*
  • NOTE: Abbreviations: APs, antipsychotics; ECG, electrocardiogram; QTc, QT interval; SNF, skilled nursing facility. *N = 60; patients who were discharged on APs during index admission and were readmitted within 1 year from discharge (only first readmission was included). Denominator = 31; number of patients whom APs from index admission were continued during readmission. Denominator = 18; number of patients who were started on APs during readmission. Denominator = 17; number of patients ECG was performed prior to APs administration; ∥Denominator = 4; number of patients ECG was performed after APs administration. Denominator = 55; number of patients who were alive during readmission

Age, mean (range), yr 81.3 (65.599.7)
Gender, no. (%)
Male 36 (60)
Female 24 (40)
Admitted from, no. (%)
Home 33 (55)
Rehabilitation facilities 5 (8)
SNF 22 (37)
Services, no. (%)
Medicine 48 (80)
Surgery 12 (20)
Types of APs continued on readmission (from index admission), no. (%)
Quetiapine 19 (61)
Olanzapine 6 (19)
Risperidone 4 (13)
Haloperidol 2 (7)
Types of APs started during readmission, no. (%)
Quetiapine 7 (39)
Risperidone 2 (11)
Haloperidol 16 (89)
Indications for AP use, no. (%)
Delirium 14 (77)
Undocumented 3 (17)
Other 1 (6)
ECG, no. (%)
Prior to APs administration 17 (94)
After APs administration 4 (22)
QTc prolongation >500 ms, no. (%)
Prior to APs administration 3 (18)
After APs administration∥ 2 (50)
Discharge destination, no. (%)
Home 23 (38)
Rehabilitation facilities 4 (7)
SNF 28 (47)
Death 5 (8)
Figure 2
Flowchart showing the outcomes of patients who were discharged on antipsychotics during index hospitalization and readmitted within 1 year from discharge. Abbreviations: APs, antipsychotics.

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

Multivariable Analysis of One‐Year Mortality From Index Admission in Patients Who Were Started on Antipsychotics.
Odds Ratio 95% Confidence Interval P Value
  • NOTE: Abbreviations: AP, antipsychotic; QTc, QT interval. *For subset of patients for whom electrocardiogram was done.

Age 1.03 0.991.06 0.13
Male sex 0.87 0.501.52 0.63
Risperdal 3.53 0.6419.40 0.15
QTc prolongation after AP administration* 3.41 1.348.67 0.01
Presence of geriatric psychiatry consult 0.30 0.091.04 0.06
Discharged to postacute facilities vs home 2.28 1.104.73 0.03

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.

Delirium, a clinical syndrome characterized by inattention and acute cognitive dysfunction, is very common in older hospitalized patients, with a reported incidence of 18% to 35% at time of admission and overall occurrence rates of 29% to 64%.[1] Previous studies have reported that a diagnosis of delirium is not benign and is associated with other adverse outcomes including prolonged hospitalization, institutionalization, increased cost, and mortality. These outcomes occurred independent of age, prior cognitive functioning, and comorbidities.[2] Guidelines recommend that management of inpatient delirium should be focused on addressing the underlying etiology and managed with nonpharmacological interventions whenever possible.[3, 4, 5] However, implementing these recommendations can prove to be very challenging in hospital settings. Providers frequently have to resort to medical therapies, including antipsychotics (APs). Although these medications are commonly used to treat delirium in elderly patients, there is limited evidence to support their efficacy, and there are currently no proven pharmacological alternatives to these medications.[6] Furthermore, previous studies have demonstrated an increased risk of stroke, infection, cognitive impairment, and mortality in elders with dementia who receive long‐term AP therapy.[7, 8, 9] Yet as many as 48% of hospitalized elders who were newly started on APs had these drugs continued at time of discharge.[10]

There have been few studies describing the long‐term outcomes of elderly patient who are started on APs in the hospital. Most information on outcomes comes from patients with dementia. Therefore, we studied the 1‐year outcomes of a cohort of patients with and without dementia who were started on APs in the hospital and then discharged on these medications. In this cohort, we aimed to describe the number of readmissions, reasons for readmissions, duration of AP therapy, use of other sedating medications such as anxiolytics, hypnotics, and antihistamines as well as the incidence of readmission and death 1 year after the index hospital discharge.

METHODS

We previously described a retrospective cohort of 300 elders (65 years old) admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013 who were newly prescribed APs while hospitalized.[10] Of patients alive at the time of discharge (260), 56% (146 patients) were discharged on APs. Two investigators extracted these 148 patient charts independently to identify and quantify the number of readmissions to the index hospital. We then limited the sample to only the first readmission per patient following the index admission and extracted this readmission for each patient. We first determined if APs were present on the admission medication reconciliation. If APs were not present on admission, we examined whether they were resumed during the hospitalization using the electronic medication administration summary. If they were present on admission, we looked to see if they were discontinued during the readmission and if additional new APs were started during the hospitalizations. We documented the circumstances around APs use and identified patients who died during their hospitalizations. We identified delirium using the same terms that were described in our prior study on the same cohort of patients.[10] We determined if patients were delirious using a predetermined algorithm (Figure 1). Briefly, we first determined delirium was documented. We then examined whether there was a Confusion Assessment Method (CAM) instrument included in the record. If a CAM instrument was not documented, we then looked for documentation using specific terms (eg, disorientation, confusions). We identified patients with dementia by determining whether dementia was documented along with other admission medical comorbidities. If it was not, we determined whether dementia was newly diagnosed during the hospital stay using progress notes or consultation notes. We did not objectively define criteria for diagnosis of dementia. We used the National Death Index (NDI) to determine mortality for all patients 1 year after discharge from the index hospitalization. The NDI is a national database of death records maintained by the National Center for Health Statistics. It has shown consistently high sensitivity and specificity for detection of death.[11]

Figure 1
Methodology for defining delirium during chart review. Abbreviations: CAM, Confusion Assessment Method.

We used descriptive statistics (means, standard deviations, range, and percents as appropriate to the scale of measurement) to describe the patient sample. We then used multiple logistic regression to identify significant predictors of death within 1 year of discharge.[12] Univariate analysis was used to select candidates for the logistic model (t tests for continuous factors and 2 for discrete factors). All factors with a significance level <0.2 on univariate analysis were included in the logistic regression, in addition to age and sex (regardless of significance). A maximum likelihood procedure was used to calculate the regression coefficients for the logistic model. The likelihood ratio criterion was used to determine the significance of individual factors in the regression model.[13] Factors with a significance level of 0.15 or less were retained in the final model, in addition to age and sex.

RESULTS

The 260 patients discharged alive from their index admissions had a 1‐year mortality rate of 29% (75/260). Of the 146/260 patients discharged on APs, 60 (41%) patients experienced at least 1 readmission (mean = 2 readmissions per patient; range, 18, with 111 total readmissions for 60 patients) within 1 year from discharge (Figure 2). Most common diagnoses at the time of readmissions were related neurological and psychiatric disorders (14%), cardiovascular and circulation disorders (13%), renal injury and electrolyte disorders (11%), and infections (6%). Among patients with at least 1 readmission, the mean age was 81.3 (range, 65.599.7), 60% were male, and 45% were admitted from a skilled nursing facility or rehabilitation facility (Table 1). Median time to readmission was 43.5 days (range, 1343 days), and 79% were readmitted to a medical service. The remaining 20% were admitted to a surgical service. Inpatient mortality during first readmissions was 8% (5/60). At the time of first readmission, 39/60 (65%) of patients were still on the same APs on which they had been discharged, and the APs were continued during the hospitalization in 79% of the patients (61% quetiapine, 19% olanzapine, and 13% risperidone). About half of patients whose APs were discontinued prior to readmission received a new AP during their hospital stays (9/20; 45%). One patient had been started on quetiapine in the outpatient setting. No patients were found to have new benzodiazepines, nonbenzodiazepine hypnotic, or antihistamines on their admission medication list.

Demographic Data of the Patients Who Were Discharged on APs and Were Readmitted the First Time Within One Year From Discharge and Circumstances Surrounding APs Initiation During Readmission
Variables Value*
  • NOTE: Abbreviations: APs, antipsychotics; ECG, electrocardiogram; QTc, QT interval; SNF, skilled nursing facility. *N = 60; patients who were discharged on APs during index admission and were readmitted within 1 year from discharge (only first readmission was included). Denominator = 31; number of patients whom APs from index admission were continued during readmission. Denominator = 18; number of patients who were started on APs during readmission. Denominator = 17; number of patients ECG was performed prior to APs administration; ∥Denominator = 4; number of patients ECG was performed after APs administration. Denominator = 55; number of patients who were alive during readmission

Age, mean (range), yr 81.3 (65.599.7)
Gender, no. (%)
Male 36 (60)
Female 24 (40)
Admitted from, no. (%)
Home 33 (55)
Rehabilitation facilities 5 (8)
SNF 22 (37)
Services, no. (%)
Medicine 48 (80)
Surgery 12 (20)
Types of APs continued on readmission (from index admission), no. (%)
Quetiapine 19 (61)
Olanzapine 6 (19)
Risperidone 4 (13)
Haloperidol 2 (7)
Types of APs started during readmission, no. (%)
Quetiapine 7 (39)
Risperidone 2 (11)
Haloperidol 16 (89)
Indications for AP use, no. (%)
Delirium 14 (77)
Undocumented 3 (17)
Other 1 (6)
ECG, no. (%)
Prior to APs administration 17 (94)
After APs administration 4 (22)
QTc prolongation >500 ms, no. (%)
Prior to APs administration 3 (18)
After APs administration∥ 2 (50)
Discharge destination, no. (%)
Home 23 (38)
Rehabilitation facilities 4 (7)
SNF 28 (47)
Death 5 (8)
Figure 2
Flowchart showing the outcomes of patients who were discharged on antipsychotics during index hospitalization and readmitted within 1 year from discharge. Abbreviations: APs, antipsychotics.

Eighteen patients received 1 or more new APs during the readmission hospitalizations. These included haloperidol (89%) and quetiapine (39%). Delirium was the main reported indication for starting APs (78%), but in 17% of cases no indication was documented. An electrocardiogram (ECG) was performed in 94% prior to APs administration and for 22% after APs administration. Corrected QT interval (QTc) of >500 ms was present in 18% of patients in pretreatment ECG and 50% of patients in post‐AP ECG. Of patients who survived readmission, 58% (32/55) were discharged to postacute facilities. Of the 39 patients who were on the same APs from index admission, 27 (69%) patients were eventually discharged on the same APs or new APs started during the readmission.

In the multivariable model (Table 2), predictors of death at 1 year included discharge to postacute facilities after index admission (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.10‐4.73, P = 0.03) and QTc prolongation >500 ms during index admission (OR: 3.41; 95% CI: 1.34‐8.67, P = 0.01). Age and gender were not associated with 1‐year mortality.

Multivariable Analysis of One‐Year Mortality From Index Admission in Patients Who Were Started on Antipsychotics.
Odds Ratio 95% Confidence Interval P Value
  • NOTE: Abbreviations: AP, antipsychotic; QTc, QT interval. *For subset of patients for whom electrocardiogram was done.

Age 1.03 0.991.06 0.13
Male sex 0.87 0.501.52 0.63
Risperdal 3.53 0.6419.40 0.15
QTc prolongation after AP administration* 3.41 1.348.67 0.01
Presence of geriatric psychiatry consult 0.30 0.091.04 0.06
Discharged to postacute facilities vs home 2.28 1.104.73 0.03

DISCUSSION

In a cohort of elderly patients who were discharged on APs, nearly one‐third (29%) died within 1 year of the hospitalization in which APs were initiated. Nearly half of the survivors from the index admission (41%) experienced at least 1 admission within 1 year from discharge. Of readmitted patients, two‐thirds were taking the same APs that had been started during the index hospitalization. Half of the patients not on APs on readmission were started on an AP during the hospitalization, most often because they became delirious on return to the acute care setting. Compared to patients discharged home after an index admission, patients who were discharged to postacute facilities were almost 4 times as likely to die during the year subsequent to the admission. These data suggest that once patients are started on APs, most are continued on them until the next admission or are restarted during that readmission. Moreover, hospitalized elders who require an AP are at high risk for mortality in the coming year.

Prior studies have reported that patients with delirium have elevated 1‐year mortality rates.[14, 15, 16, 17, 18, 19] A secondary analysis of the Delirium Prevention Trial, which included 437 hospitalized older patients, revealed a 1‐year mortality rate of 20% in those who were never delirious during hospitalization, compared to 26% to 38% in patients with delirium.[19] Additionally, 1‐year mortality in hospitalized older patients with delirium (36%) was shown to be higher than patients with dementia (29%) or depression (26%).[17] Unlike these studies, not all of the patients in our study had documented delirium, but all received an AP. Still, it is notable that the 1‐year mortality rate for delirium in general is similar to what we found in this study.

The literature has also reported that long‐term AP use is associated with excess mortality in elder patients, especially those with dementia.[20, 21, 22] In a retrospective cohort study, older patients with dementia who were taking antipsychotics had significantly higher 1‐year mortality rates (23%29%) than patients not taking antipsychotic medications (15%). In a large Canadian propensity score‐matched cohort study that included over 13,000 demented older adults, the mortality was higher in the community‐dwelling elders who received atypical APs compared to no APs, with a difference of 1.1% in 180‐day mortality rate after initiation of APs.[21] The absolute mortality rate was 2.6% higher in patients who received typical compared to atypical APs. Unlike these studies, not every patient in our cohort had a diagnosis of dementia, but again, mortality rates in these studies appear similar to our cohort.

In contrast, other observational studies have not found an increased risk associated with receipt of APs. For example, a prospective study that enrolled approximately 950 patients with probable dementia showed that AP use was not associated with time to death after adjustment for comorbidities, demographic and cognitive variables.[23] These conflicting results highlight the difficulties of attributing outcomes in high‐risk populations. Although the excess mortality observed in patients taking APs may be related to the risks of APs, it is quite possible that patients who require APs (most often for delirium or agitated dementia) are at higher risk of death. This confounding by indication may be nearly impossible to adjust for retrospectively, even using techniques such as propensity matching.

Our report adds to the literature; we know of no studies to date describing a cohort of patients, most with delirium, who were started on APs in the hospital. We also attempted to identify the reasons that patients were started on APs, which have been infrequently reported. As noted above, our 1‐year mortality rate of 29% among older patients prescribed APs in the hospital was quite similar to mortality rates both for patients with delirium who were not necessarily treated with APs and patients with dementia who were treated with APs. This finding further supports the argument that risk factors for mortality, including dementia, delirium, and AP use are very difficult to tease apart. It is possible that the reasons that APs are prescribed (agitated delirium or dementia) have as much to do with the excess mortality reported in observational studies of APs as the use of APs themselves.

The high rate of continued AP use we observed (two‐thirds of readmitted patients) may reflect limited pharmacological alternatives to these medications with little evidence to support treating the symptoms of delirium with other drug classes, along with suboptimal environmental and behavioral modifications in postacute facilities and hospitals. This is unfortunate given that delirium is often preventable. Systematic implementation of well‐documented strategies to decrease delirium in hospitals and postacute facilities would likely reduce the prescription of APs and has the potential to slow the decline in this vulnerable population. A meta‐analysis incorporating both randomized and nonrandomized trials of medical and surgical patients showed that multicomponent nonpharmacologic interventions decreased delirium by 50%.[24] Thus, simple interventions such as reorientation, early mobilization, optimizing vision and hearing, sleepwake cycle preservation, and hydration might avoid roughly 1 million cases of delirium in hospitalized older adults annually.[24] The Hospital Elder Life Program and Acute Care for Elders units are examples of programs that have been shown to decrease the incidence of delirium.[25, 26]

Despite vigorous efforts to prevent delirium, a subgroup of patients still will become delirious. These patients are at high risk for death. Our mortality prediction model revealed that patients who were discharged to postacute facilities were 4 times more likely to die during the subsequent year compared to patients who were discharged home. Patients discharged to postacute facilities are likely to have a higher burden of disease, greater functional and cognitive impairment, and more frailty than those who are able to return to the community. Very ill and/or frail patients receiving APs in the hospital and requiring APs on discharge to postacute care facilities have limited survival and may benefit from expedited palliative care interventions to clarify prognosis and goals, and relieve suffering. At a minimum, our study identifies a need for further study to identify this very high‐risk group of elders. It is notable that 50% of patients were found to have a post‐treatment ECG with a QTc of >500 ms, a finding that has not been previously described. This would put these patients at higher risk of mortality, and as such we suggest that current guidelines should continue to emphasize the importance of post‐treatment ECGs and set clear criteria for discontinuation in elderly patients.

Our study is limited by its retrospective, single‐center design and small sample size, therefore limiting the interpretation and generalizability of the results to other hospitals. Quetiapine was the most common AP medication used in our hospital; therefore, our findings cannot be generalized to hospitals that utilize other AP agents. Future studies should examine antipsychotic use across hospitals to determine variation in prescribing patterns and outcomes. Nevertheless, the care of these patients were transitioned to a large number of geriatricians and primary care and nursing home physicians after discharge, and the reflected practice patterns extended beyond our hospital. Additionally, we were unable to determine when and why APs were discontinued or started in the outpatient setting. We were only able to detect readmissions to the 3 hospitals within our health system and therefore may have missed some readmissions to other institutions, although the majority of patients in our region tend to return to the same hospital. For patients who were not readmitted, we were also unable to identify whether they remained on the APs initiated during their index hospitalizations. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.[10] We were unable to determine the types of delirium (hyperactive vs hypoactive) given that the documentations on behavioral symptoms were largely missing from the charts. The number of patients with preexisting diagnosis of dementia was likely underestimated, as we were only able to verify the diagnosis from the medical history. Additionally, the retrospective design based on chart review limited the factors that we could detect and grade accurately for inclusion in our mortality prediction model. Of note, our model did not contain objective measures of cognition, agitation, function, and markers for frailty such as walking speed, weak grip strength, weight loss, and low physical activity.

CONCLUSION

Initiating an AP (eg, haloperidol, quetiapine, olanzapine, and risperidone) in the hospital is likely to result in long‐term use of these medications despite the fact that AP use has been associated with multiple risks including falls, fractures, stroke, cardiovascular disease, and increased mortality in those with underlying dementia.[27] When possible, behavioral interventions to prevent delirium and slow the trajectory of decline should be implemented to reduce AP use. If patients with delirium are started on antipsychotics, it is important to monitor for prolonged QTc given the associated risk of mortality. In a subgroup of patients at high risk for death in the upcoming year, occurrence of delirium or use of APs during a hospitalization should both be considered triggers for early advance care planning and possibly palliative care and end‐of‐life discussions, with an emphasis on quality of life.

Disclosures: The research was supported by the Department of Medicine, Baystate Medical Center/Tufts University School of Medicine. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh, Ramdass, and Ms. Garb acquired the data. Ms Garb analyzed and interpreted the data. Drs. Loh, Ramdass, and Thim drafted the manuscript. Drs. Brennan, Lindenauer, and Lagu and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu has received consulting fees from the Institute for Healthcare Improvement, under contract to the Centers for Medicare and Medicaid Services, for her work on a project to help health systems achieve disability competence. Dr. Brennan is supported by a Geriatric Work Force Enhancement Grant from the US Department of Health and Human Services award number 1 U1QHP287020100. The authors report no conflicts of interest.

References
  1. Inouye SK, Westendorp RGJ, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911922.
  2. Fong TG, Jones RN, Marcantonio ER, et al. Adverse outcomes after hospitalization and delirium in persons with Alzheimer disease. Ann Intern Med. 2012;156:848856, W296.
  3. American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults. American Geriatrics Society abstracted clinical practice guideline for postoperative delirium in older adults. J Am Geriatr Soc. 2015;63:142150.
  4. Practice guideline for the treatment of patients with delirium. American Psychiatric Association. Am J Psychiatry. 1999;156:120.
  5. Potter J, George J; Guideline Development Group. The prevention, diagnosis and management of delirium in older people: concise guidelines. Clin Med (Lond). 2006;6:303308.
  6. Seitz DP, Gill SS, Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:1121.
  7. Maust DT, Kim HM, Seyfried LS, et al. Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. JAMA Psychiatry. 2015;72:438445.
  8. Andrade C, Radhakrishnan R. Safety and efficacy of antipsychotic drugs for the behavioral and psychological symptoms of dementia. Indian J Psychiatry. 2009;51(suppl 1):S87S92.
  9. Gareri P, Fazio P, Manfredi VGL, Sarro G. Use and safety of antipsychotics in behavioral disorders in elderly people with dementia. J Clin Psychopharmacol. 2014;34:109123.
  10. Loh KP, Ramdass S, Garb JL, Brennan MJ, Lindenauer PK, Lagu T. From hospital to community: use of antipsychotics in hospitalized elders. J Hosp Med. 2014;9:802804.
  11. Sesso HD, Paffenbarger RS, Lee I‐M. Comparison of National Death Index and World Wide Web Death Searches. Am J Epidemiol. 2000;152:107111.
  12. Cox DR. Analysis of Binary Data. London, United Kingdom: Methuen; 1970:7699.
  13. Lee ET. Statistical Methods for Survival Data Analysis. New York, NY: John Wiley 1992:233236.
  14. Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014;43:127132.
  15. Grover S, Ghormode D, Ghosh A, et al. Risk factors for delirium and inpatient mortality with delirium. J Postgrad Med. 2013;59:263270.
  16. Avelino‐Silva TJ, Farfel JM, Curiati JAE, Amaral JRG, Campora F, Jacob‐Filho W. Comprehensive geriatric assessment predicts mortality and adverse outcomes in hospitalized older adults. BMC Geriatr. 2014;14:129.
  17. Tsai M‐C, Weng H‐H, Chou S‐Y, Tsai C‐S, Hung T‐H, Su J‐A. One‐year mortality of elderly inpatients with delirium, dementia, or depression seen by a consultation‐liaison service. Psychosomatics. 2012;53:433438.
  18. Hemert AM, Mast RC, Hengeveld MW, Vorstenbosch M. Excess mortality in general hospital patients with delirium: a 5‐year follow‐up of 519 patients seen in psychiatric consultation. J Psychosom Res. 1994;38:339346.
  19. McAvay GJ, Ness PH, Bogardus ST, et al. Older adults discharged from the hospital with delirium: 1‐year outcomes. J Am Geriatr Soc. 2006;54:12451250.
  20. Ballard C, Hanney ML, Theodoulou M, et al. The dementia antipsychotic withdrawal trial (DART‐AD): long‐term follow‐up of a randomised placebo‐controlled trial. Lancet Neurol. 2009;8:151157.
  21. Gill SS, Bronskill SE, Normand S‐LT, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775786.
  22. Wang PS, Schneeweiss S, Avorn J, et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:23352341.
  23. Lopez OL, Becker JT, Chang Y‐F, et al. The long‐term effects of conventional and atypical antipsychotics in patients with probable Alzheimer's disease. Am J Psychiatry. 2013;170:10511058.
  24. Hshieh TT, Yue J, Oh E, et al. Effectiveness of multicomponent nonpharmacological delirium interventions: a meta‐analysis. JAMA Intern Med. 2015;175:512520.
  25. Rubin FH, Neal K, Fenlon K, Hassan S, Inouye SK. Sustainability and scalability of the hospital elder life program at a community hospital. J Am Geriatr Soc. 2011;59:359365.
  26. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60:22372245.
  27. Muench J, Hamer AM. Adverse effects of antipsychotic medications. Am Fam Physician. 2010;81:617622.
References
  1. Inouye SK, Westendorp RGJ, Saczynski JS. Delirium in elderly people. Lancet. 2014;383:911922.
  2. Fong TG, Jones RN, Marcantonio ER, et al. Adverse outcomes after hospitalization and delirium in persons with Alzheimer disease. Ann Intern Med. 2012;156:848856, W296.
  3. American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults. American Geriatrics Society abstracted clinical practice guideline for postoperative delirium in older adults. J Am Geriatr Soc. 2015;63:142150.
  4. Practice guideline for the treatment of patients with delirium. American Psychiatric Association. Am J Psychiatry. 1999;156:120.
  5. Potter J, George J; Guideline Development Group. The prevention, diagnosis and management of delirium in older people: concise guidelines. Clin Med (Lond). 2006;6:303308.
  6. Seitz DP, Gill SS, Zyl LT. Antipsychotics in the treatment of delirium: a systematic review. J Clin Psychiatry. 2007;68:1121.
  7. Maust DT, Kim HM, Seyfried LS, et al. Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. JAMA Psychiatry. 2015;72:438445.
  8. Andrade C, Radhakrishnan R. Safety and efficacy of antipsychotic drugs for the behavioral and psychological symptoms of dementia. Indian J Psychiatry. 2009;51(suppl 1):S87S92.
  9. Gareri P, Fazio P, Manfredi VGL, Sarro G. Use and safety of antipsychotics in behavioral disorders in elderly people with dementia. J Clin Psychopharmacol. 2014;34:109123.
  10. Loh KP, Ramdass S, Garb JL, Brennan MJ, Lindenauer PK, Lagu T. From hospital to community: use of antipsychotics in hospitalized elders. J Hosp Med. 2014;9:802804.
  11. Sesso HD, Paffenbarger RS, Lee I‐M. Comparison of National Death Index and World Wide Web Death Searches. Am J Epidemiol. 2000;152:107111.
  12. Cox DR. Analysis of Binary Data. London, United Kingdom: Methuen; 1970:7699.
  13. Lee ET. Statistical Methods for Survival Data Analysis. New York, NY: John Wiley 1992:233236.
  14. Evans SJ, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014;43:127132.
  15. Grover S, Ghormode D, Ghosh A, et al. Risk factors for delirium and inpatient mortality with delirium. J Postgrad Med. 2013;59:263270.
  16. Avelino‐Silva TJ, Farfel JM, Curiati JAE, Amaral JRG, Campora F, Jacob‐Filho W. Comprehensive geriatric assessment predicts mortality and adverse outcomes in hospitalized older adults. BMC Geriatr. 2014;14:129.
  17. Tsai M‐C, Weng H‐H, Chou S‐Y, Tsai C‐S, Hung T‐H, Su J‐A. One‐year mortality of elderly inpatients with delirium, dementia, or depression seen by a consultation‐liaison service. Psychosomatics. 2012;53:433438.
  18. Hemert AM, Mast RC, Hengeveld MW, Vorstenbosch M. Excess mortality in general hospital patients with delirium: a 5‐year follow‐up of 519 patients seen in psychiatric consultation. J Psychosom Res. 1994;38:339346.
  19. McAvay GJ, Ness PH, Bogardus ST, et al. Older adults discharged from the hospital with delirium: 1‐year outcomes. J Am Geriatr Soc. 2006;54:12451250.
  20. Ballard C, Hanney ML, Theodoulou M, et al. The dementia antipsychotic withdrawal trial (DART‐AD): long‐term follow‐up of a randomised placebo‐controlled trial. Lancet Neurol. 2009;8:151157.
  21. Gill SS, Bronskill SE, Normand S‐LT, et al. Antipsychotic drug use and mortality in older adults with dementia. Ann Intern Med. 2007;146:775786.
  22. Wang PS, Schneeweiss S, Avorn J, et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353:23352341.
  23. Lopez OL, Becker JT, Chang Y‐F, et al. The long‐term effects of conventional and atypical antipsychotics in patients with probable Alzheimer's disease. Am J Psychiatry. 2013;170:10511058.
  24. Hshieh TT, Yue J, Oh E, et al. Effectiveness of multicomponent nonpharmacological delirium interventions: a meta‐analysis. JAMA Intern Med. 2015;175:512520.
  25. Rubin FH, Neal K, Fenlon K, Hassan S, Inouye SK. Sustainability and scalability of the hospital elder life program at a community hospital. J Am Geriatr Soc. 2011;59:359365.
  26. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60:22372245.
  27. Muench J, Hamer AM. Adverse effects of antipsychotic medications. Am Fam Physician. 2010;81:617622.
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Antipsychotics in Hospitalized Elders

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From hospital to community: Use of antipsychotics in hospitalized elders

Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1
Methodology for defining Delirium during chart review

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Demographic Data and Circumstances Surrounding AP Medication Initiation
Variable N (%), Total=300
  • NOTE: Abbreviations: AP, antipsychotics; CAM, confusion assessment method; ECG, electrocardiography; QTc, QT interval; SNF, skilled nursing facility.

  • Denominator=249; number of patients on whom APs were started for delirium.

  • Denominator=265; number of patients with ECG performed prior to APs administration.

  • Denominator=157; number of patients with ECG performed after APs administration.

  • Denominator=297; 3 patients transferred to other acute care hospitals were excluded.

Gender
Male 161 (54)
Female 139 (46)
Inpatient mortality rate 30 (10)
Services
Medicine 202 (67)
Surgery 98 (33)
Indication for APs use
Delirium 249 (83)
Hallucinations 19 (6)
Anxiety 20 (7)
Other 38 (13)
Atypical APs
Quetiapine 257 (86)
Olanzapine 29 (10)
Risperidone 26 (9)
Typical APs
Haloperidol 166 (55)
Thorazine 4 (1)
Use of CAM 79 (32)a
Physical restraints 89 (30)
Documented or suspected dementia 134 (45)
Geriatrics consults 120 (40)
Psychiatric consults 29 (10)
ECG
Prior to APs administration 265 (88)
After APs administration 157 (52)
QTc prolongation >500 ms
Prior to APs administration 41 (15)b
After APs administration 39 (25)c
Admitted from SNF 36 (12)
Discharge destination
Home 68 (23)
SNFs, short and long‐term rehabilitations 199 (66)
Transfer to other acute care hospitals 3 (1)
Continuation of APs at discharge 143 (48)d

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

Acknowledgements

The authors acknowledge Mihaela Stefan, MD, FACP, for her comments on an earlier draft of this manuscript.

Disclosures: Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh and Ramdass acquired the data. Ms. Garb analyzed and interpreted the data. Dr. Loh drafted the manuscript. Drs Brennan, Lindenauer, and Lagu, and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. The authors report no conflicts of interest.

Files
References
  1. Witlox J, Eurelings LSM, Jonghe JFM, Kalisvaart KJ, Eikelenboom P, Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis. JAMA. 2010;304(4):443451.
  2. Flores L. Off‐label use of antipsychotics for dementia patients discouraged. The Hospitalist. November 2012\http://www.the‐hospitalist.org/details/article/2785121/Off‐Label_Use_of_Antipsychotics_for_Dementia_Patients_Discouraged.html. Accessed June 29, 2014.
  3. STATA/MP [computer program]. Version 13.1 for Windows. College Station, TX: StataCorp; 2013.
  4. Rothberg MB, Herzig SJ, Pekow PS, Avrunin J, Lagu T, Lindenauer PK. Association between sedating medications and delirium in older inpatients. J Am Geriatr Soc. 2013;61(6):923930.
  5. Wang W, Li H‐L, Wang D‐X, et al. Haloperidol prophylaxis decreases delirium incidence in elderly patients after noncardiac surgery: a randomized controlled trial. Crit Care Med. 2012;40(3):731739.
  6. Shah AA, Aftab A, Coverdale J. QTc prolongation with antipsychotics: is routine ECG monitoring recommended? J Psychiatr Pract. 2014;20(3):196206.
  7. Schneeweiss S, Setoguchi S, Brookhart A, Dormuth C, Wang PS. Risk of death associated with the use of conventional versus atypical antipsychotic drugs among elderly patients. CMAJ. 2007;176(5):627632.
  8. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60(12):22372245.
  9. Inouye SK, Bogardus ST, Baker DI, Leo‐Summers L, Cooney LM. The Hospital Elder Life Program: a model of care to prevent cognitive and functional decline in older hospitalized patients. J Am Geriatr Soc. 2000;48(12):16971706.
  10. Cole MG, Ciampi A, Belzile E, Zhong L. Persistent delirium in older hospital patients: a systematic review of frequency and prognosis. Age Ageing. 2009;38(1):1926.
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Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1
Methodology for defining Delirium during chart review

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Demographic Data and Circumstances Surrounding AP Medication Initiation
Variable N (%), Total=300
  • NOTE: Abbreviations: AP, antipsychotics; CAM, confusion assessment method; ECG, electrocardiography; QTc, QT interval; SNF, skilled nursing facility.

  • Denominator=249; number of patients on whom APs were started for delirium.

  • Denominator=265; number of patients with ECG performed prior to APs administration.

  • Denominator=157; number of patients with ECG performed after APs administration.

  • Denominator=297; 3 patients transferred to other acute care hospitals were excluded.

Gender
Male 161 (54)
Female 139 (46)
Inpatient mortality rate 30 (10)
Services
Medicine 202 (67)
Surgery 98 (33)
Indication for APs use
Delirium 249 (83)
Hallucinations 19 (6)
Anxiety 20 (7)
Other 38 (13)
Atypical APs
Quetiapine 257 (86)
Olanzapine 29 (10)
Risperidone 26 (9)
Typical APs
Haloperidol 166 (55)
Thorazine 4 (1)
Use of CAM 79 (32)a
Physical restraints 89 (30)
Documented or suspected dementia 134 (45)
Geriatrics consults 120 (40)
Psychiatric consults 29 (10)
ECG
Prior to APs administration 265 (88)
After APs administration 157 (52)
QTc prolongation >500 ms
Prior to APs administration 41 (15)b
After APs administration 39 (25)c
Admitted from SNF 36 (12)
Discharge destination
Home 68 (23)
SNFs, short and long‐term rehabilitations 199 (66)
Transfer to other acute care hospitals 3 (1)
Continuation of APs at discharge 143 (48)d

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

Acknowledgements

The authors acknowledge Mihaela Stefan, MD, FACP, for her comments on an earlier draft of this manuscript.

Disclosures: Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh and Ramdass acquired the data. Ms. Garb analyzed and interpreted the data. Dr. Loh drafted the manuscript. Drs Brennan, Lindenauer, and Lagu, and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. The authors report no conflicts of interest.

Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1
Methodology for defining Delirium during chart review

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Demographic Data and Circumstances Surrounding AP Medication Initiation
Variable N (%), Total=300
  • NOTE: Abbreviations: AP, antipsychotics; CAM, confusion assessment method; ECG, electrocardiography; QTc, QT interval; SNF, skilled nursing facility.

  • Denominator=249; number of patients on whom APs were started for delirium.

  • Denominator=265; number of patients with ECG performed prior to APs administration.

  • Denominator=157; number of patients with ECG performed after APs administration.

  • Denominator=297; 3 patients transferred to other acute care hospitals were excluded.

Gender
Male 161 (54)
Female 139 (46)
Inpatient mortality rate 30 (10)
Services
Medicine 202 (67)
Surgery 98 (33)
Indication for APs use
Delirium 249 (83)
Hallucinations 19 (6)
Anxiety 20 (7)
Other 38 (13)
Atypical APs
Quetiapine 257 (86)
Olanzapine 29 (10)
Risperidone 26 (9)
Typical APs
Haloperidol 166 (55)
Thorazine 4 (1)
Use of CAM 79 (32)a
Physical restraints 89 (30)
Documented or suspected dementia 134 (45)
Geriatrics consults 120 (40)
Psychiatric consults 29 (10)
ECG
Prior to APs administration 265 (88)
After APs administration 157 (52)
QTc prolongation >500 ms
Prior to APs administration 41 (15)b
After APs administration 39 (25)c
Admitted from SNF 36 (12)
Discharge destination
Home 68 (23)
SNFs, short and long‐term rehabilitations 199 (66)
Transfer to other acute care hospitals 3 (1)
Continuation of APs at discharge 143 (48)d

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

Acknowledgements

The authors acknowledge Mihaela Stefan, MD, FACP, for her comments on an earlier draft of this manuscript.

Disclosures: Drs. Lagu and Loh had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the analysis. Drs. Loh, Brennan, Lindenauer, and Lagu conceived of the study. Drs. Loh and Ramdass acquired the data. Ms. Garb analyzed and interpreted the data. Dr. Loh drafted the manuscript. Drs Brennan, Lindenauer, and Lagu, and Ms. Garb critically reviewed the manuscript for important intellectual content. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. The authors report no conflicts of interest.

References
  1. Witlox J, Eurelings LSM, Jonghe JFM, Kalisvaart KJ, Eikelenboom P, Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis. JAMA. 2010;304(4):443451.
  2. Flores L. Off‐label use of antipsychotics for dementia patients discouraged. The Hospitalist. November 2012\http://www.the‐hospitalist.org/details/article/2785121/Off‐Label_Use_of_Antipsychotics_for_Dementia_Patients_Discouraged.html. Accessed June 29, 2014.
  3. STATA/MP [computer program]. Version 13.1 for Windows. College Station, TX: StataCorp; 2013.
  4. Rothberg MB, Herzig SJ, Pekow PS, Avrunin J, Lagu T, Lindenauer PK. Association between sedating medications and delirium in older inpatients. J Am Geriatr Soc. 2013;61(6):923930.
  5. Wang W, Li H‐L, Wang D‐X, et al. Haloperidol prophylaxis decreases delirium incidence in elderly patients after noncardiac surgery: a randomized controlled trial. Crit Care Med. 2012;40(3):731739.
  6. Shah AA, Aftab A, Coverdale J. QTc prolongation with antipsychotics: is routine ECG monitoring recommended? J Psychiatr Pract. 2014;20(3):196206.
  7. Schneeweiss S, Setoguchi S, Brookhart A, Dormuth C, Wang PS. Risk of death associated with the use of conventional versus atypical antipsychotic drugs among elderly patients. CMAJ. 2007;176(5):627632.
  8. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60(12):22372245.
  9. Inouye SK, Bogardus ST, Baker DI, Leo‐Summers L, Cooney LM. The Hospital Elder Life Program: a model of care to prevent cognitive and functional decline in older hospitalized patients. J Am Geriatr Soc. 2000;48(12):16971706.
  10. Cole MG, Ciampi A, Belzile E, Zhong L. Persistent delirium in older hospital patients: a systematic review of frequency and prognosis. Age Ageing. 2009;38(1):1926.
References
  1. Witlox J, Eurelings LSM, Jonghe JFM, Kalisvaart KJ, Eikelenboom P, Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis. JAMA. 2010;304(4):443451.
  2. Flores L. Off‐label use of antipsychotics for dementia patients discouraged. The Hospitalist. November 2012\http://www.the‐hospitalist.org/details/article/2785121/Off‐Label_Use_of_Antipsychotics_for_Dementia_Patients_Discouraged.html. Accessed June 29, 2014.
  3. STATA/MP [computer program]. Version 13.1 for Windows. College Station, TX: StataCorp; 2013.
  4. Rothberg MB, Herzig SJ, Pekow PS, Avrunin J, Lagu T, Lindenauer PK. Association between sedating medications and delirium in older inpatients. J Am Geriatr Soc. 2013;61(6):923930.
  5. Wang W, Li H‐L, Wang D‐X, et al. Haloperidol prophylaxis decreases delirium incidence in elderly patients after noncardiac surgery: a randomized controlled trial. Crit Care Med. 2012;40(3):731739.
  6. Shah AA, Aftab A, Coverdale J. QTc prolongation with antipsychotics: is routine ECG monitoring recommended? J Psychiatr Pract. 2014;20(3):196206.
  7. Schneeweiss S, Setoguchi S, Brookhart A, Dormuth C, Wang PS. Risk of death associated with the use of conventional versus atypical antipsychotic drugs among elderly patients. CMAJ. 2007;176(5):627632.
  8. Fox MT, Persaud M, Maimets I, et al. Effectiveness of acute geriatric unit care using acute care for elders components: a systematic review and meta‐analysis. J Am Geriatr Soc. 2012;60(12):22372245.
  9. Inouye SK, Bogardus ST, Baker DI, Leo‐Summers L, Cooney LM. The Hospital Elder Life Program: a model of care to prevent cognitive and functional decline in older hospitalized patients. J Am Geriatr Soc. 2000;48(12):16971706.
  10. Cole MG, Ciampi A, Belzile E, Zhong L. Persistent delirium in older hospital patients: a systematic review of frequency and prognosis. Age Ageing. 2009;38(1):1926.
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Address for correspondence and reprint requests: Kah Poh Loh, BMedSci, MB BCh BAO, Baystate Medical Center/Tufts University, 759 Chestnut Street, Springfield, MA 01199; Telephone: 413‐306‐9767; Fax: 413‐794‐2350; E‐mail: Melissalkp@gmail.com
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His mother

Bertha Johnson is back with pneumonia again. The ED doctor on the telephone sounded both matter‐of‐fact and mildly bored when I answered her page about another admission to the hospitalist service. I hadn't met Mrs. Johnson previously, but came to know her and Douglas, her only son, well over the next few days.

The Johnsons were facing a difficult choice. Bertha was now bedbound and quadriplegic following a 40‐year battle with multiple sclerosis and gradually mounting disability. She was cognitively intact and had a solid grasp of medical realities, but was hard of hearing and quite dysarthric. Forming even short phrases and sentences took great effort. However tenuous, this ability to speak allowed her to communicate with those she loved. She had been admitted thrice in the past year with aspiration pneumonia, as she was unable to clear her secretions reliably. Repeated bronchoscopies demonstrated an inability to protect her airway. Douglas, who was also her health care proxy, favored proceeding with the tracheostomy suggested by the pulmonary team. On a prior admission, he had been distressed when his mother refused this intervention. Now she was back with the identical problem and had been given the same recommendation by her doctors. It was particularly difficult for me to discuss these sensitive issues when I had not previously met either Bertha or her son. I spoke to her primary care physician over the phone and he agreed with the need for tracheostomy. The pulmonary team had been involved in discussions about tracheostomy in all of her hospital admissions, providing continuity of care in the process. Ultimately, it was my responsibility to help Bertha and Douglas come to a decision.

After multiple discussions between the Johnsons and me, a consensus emerged to proceed with the tracheostomy. We recognized that the procedure would increase her care needs and arranged for a stay in a skilled nursing facility to provide access to round‐the‐clock suctioning. The evening prior to the tracheostomy, the floor nurse and I reviewed the procedure to ensure that Bertha was fully prepared. What followed resulted in a drastic change of plan. Bertha emphasized that she did not want to lose her only means of communication, even if the surgery would prolong her life. She admitted that she reluctantly agreed to the procedure only to please her son and doctors, because they believed it to be in her best interest. Her fear of the prospect of death from drowning in her own secretions was much less than her fear of silence and isolation that would result from her loss of speech. I shared her misgivings with Douglas, and she admitted to him that she had only agreed to the procedure for his sake. We cancelled the surgery.

Douglas later revealed that he also had been ambivalent about the procedure for sometime, as it would necessitate a nursing home stay and the loss of the caretakers who had cared for his mother so wonderfully for many years. Bertha lived alone in her own home with the help of visiting nurses and patient care assistants Douglas paid for out‐of‐pocket. Douglas lived and worked in a city over a hundred miles away, but managed to visit several times a month to facilitate his mother's care. He had supported the procedure only because the doctors had said it was the only way to avoid future pneumonias. The idea of a tracheostomy was definitively abandoned once and for all.

The Johnsons wanted Bertha to return to her home, but hospital case managers felt this would be an unsafe plan of care as she was alone for several hours a day. She was largely immobile and unable to escape if there were a fire or other emergency. Also, the caretakers were not trained to use the suction equipment, and the visiting nurses would only be available intermittently. The home care staff felt they could no longer meet her needs and declined to resume her care. Douglas became very frustrated by the delays and protracted negotiations, enough so that he threatened to sue the institution for taking over my mother's life. The threat of litigation is usually a cry for help that reflects either miscommunication or the suffering of a conflicted family.

As their hospitalist, I hoped to advocate for both the patient and Douglas while coordinating the overall care plan. I had always received consistent responses from the Johnsons, but other staff members noted that Douglas had expressed shifting views on the best site of care for his mother. At Bertha's request, I convened a meeting with her, Douglas, the social worker, case manager, visiting and staff nurses, the palliative care nurse, and floor manager. Prior to this, I met with all involved health care providers to ensure we understood each other's abilities and limitations regarding Bertha's care. As I entered the room for the family meeting, I knew it was ultimately the patient's choice whether she wanted to return home or notas long as she understood the risks involved.

During the meeting, all the team members explained the dilemmas they faced in planning for a safe disposition. Douglas's response illuminated his devotion and love for his ailing mother. He had known all along that it would be less expensive and burdensome for him for Bertha to be placed in a facility. However, he feared nursing home admission represented giving up and failing to fulfill my duty to my mother. Tears ran down the face of this otherwise well composed, immaculately dressed, articulate man in his late forties. He had assumed the responsibility for his mother's care while still a child and had carried this self‐imposed moral burden his entire life. This meeting was his first opportunity to voice explicitly to the medical team his immense love and concern for his dear mother.

I gently probed to clarify Bertha's values and goals. On a brief, prior nursing home stay, Bertha had found the experience to be scary and unfamiliar. However, as her functional abilities continued to decline, her feelings had changed. She now felt lonely and anxious at home when her caretakers were absent. She actually wanted to go to a nursing home, where there would always be company and support available! She had not told Douglas this because she knew he cared for her deeply and she didn't want to hurt his feelings; he seemed committed to caring for her the same way he had for so many years.

In short, Douglas knew it would be easier for him if his mother were in a nursing facility, but assumed she wanted to stay in her home. Oddly, Bertha was only remaining at home because she believed that was what her son wished. A few days later, Bertha was transferred to a nursing home near several relatives who would visit her regularly. Douglas was again selfless in not seeking to move her closer to him. He didn't want to uproot her more than was unavoidable.

Day‐to‐day practice reveals many examples of love and dedication, but I have never seen such blinding and unquestioning commitment as exemplified by this mother‐son duo. From them, I learned the importance of attentive and active listening. Our polite patients may only subtly hint at matters of the deepest import. If we cannot truly hear their unspoken emotions, we risk harming them and misinterpreting their words and actions. Some healthcare providers had seen Douglas as aggressive and demanding with his threat of a lawsuit, whereas Bertha had been described as unrealistic or in denial. These views distorted a much more complex reality. Time and attention to careful communication between the healthcare providers, the patient, and her son bore fruit in this case. The procedure that was really needed was the family meeting and not the tracheostomy! An undesired and invasive procedure was avoided, goals of care were clarified, quality of life maximized, a safe discharge arranged, and a new mutual understanding achieved. I was humbled, and reminded of the importance of team‐based care and the need to approach each patient and family member in a receptive, nonjudgmental, and open manner. Douglas and Bertha Johnson were linked with a profound and abiding bond that would only be severed at death.

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Bertha Johnson is back with pneumonia again. The ED doctor on the telephone sounded both matter‐of‐fact and mildly bored when I answered her page about another admission to the hospitalist service. I hadn't met Mrs. Johnson previously, but came to know her and Douglas, her only son, well over the next few days.

The Johnsons were facing a difficult choice. Bertha was now bedbound and quadriplegic following a 40‐year battle with multiple sclerosis and gradually mounting disability. She was cognitively intact and had a solid grasp of medical realities, but was hard of hearing and quite dysarthric. Forming even short phrases and sentences took great effort. However tenuous, this ability to speak allowed her to communicate with those she loved. She had been admitted thrice in the past year with aspiration pneumonia, as she was unable to clear her secretions reliably. Repeated bronchoscopies demonstrated an inability to protect her airway. Douglas, who was also her health care proxy, favored proceeding with the tracheostomy suggested by the pulmonary team. On a prior admission, he had been distressed when his mother refused this intervention. Now she was back with the identical problem and had been given the same recommendation by her doctors. It was particularly difficult for me to discuss these sensitive issues when I had not previously met either Bertha or her son. I spoke to her primary care physician over the phone and he agreed with the need for tracheostomy. The pulmonary team had been involved in discussions about tracheostomy in all of her hospital admissions, providing continuity of care in the process. Ultimately, it was my responsibility to help Bertha and Douglas come to a decision.

After multiple discussions between the Johnsons and me, a consensus emerged to proceed with the tracheostomy. We recognized that the procedure would increase her care needs and arranged for a stay in a skilled nursing facility to provide access to round‐the‐clock suctioning. The evening prior to the tracheostomy, the floor nurse and I reviewed the procedure to ensure that Bertha was fully prepared. What followed resulted in a drastic change of plan. Bertha emphasized that she did not want to lose her only means of communication, even if the surgery would prolong her life. She admitted that she reluctantly agreed to the procedure only to please her son and doctors, because they believed it to be in her best interest. Her fear of the prospect of death from drowning in her own secretions was much less than her fear of silence and isolation that would result from her loss of speech. I shared her misgivings with Douglas, and she admitted to him that she had only agreed to the procedure for his sake. We cancelled the surgery.

Douglas later revealed that he also had been ambivalent about the procedure for sometime, as it would necessitate a nursing home stay and the loss of the caretakers who had cared for his mother so wonderfully for many years. Bertha lived alone in her own home with the help of visiting nurses and patient care assistants Douglas paid for out‐of‐pocket. Douglas lived and worked in a city over a hundred miles away, but managed to visit several times a month to facilitate his mother's care. He had supported the procedure only because the doctors had said it was the only way to avoid future pneumonias. The idea of a tracheostomy was definitively abandoned once and for all.

The Johnsons wanted Bertha to return to her home, but hospital case managers felt this would be an unsafe plan of care as she was alone for several hours a day. She was largely immobile and unable to escape if there were a fire or other emergency. Also, the caretakers were not trained to use the suction equipment, and the visiting nurses would only be available intermittently. The home care staff felt they could no longer meet her needs and declined to resume her care. Douglas became very frustrated by the delays and protracted negotiations, enough so that he threatened to sue the institution for taking over my mother's life. The threat of litigation is usually a cry for help that reflects either miscommunication or the suffering of a conflicted family.

As their hospitalist, I hoped to advocate for both the patient and Douglas while coordinating the overall care plan. I had always received consistent responses from the Johnsons, but other staff members noted that Douglas had expressed shifting views on the best site of care for his mother. At Bertha's request, I convened a meeting with her, Douglas, the social worker, case manager, visiting and staff nurses, the palliative care nurse, and floor manager. Prior to this, I met with all involved health care providers to ensure we understood each other's abilities and limitations regarding Bertha's care. As I entered the room for the family meeting, I knew it was ultimately the patient's choice whether she wanted to return home or notas long as she understood the risks involved.

During the meeting, all the team members explained the dilemmas they faced in planning for a safe disposition. Douglas's response illuminated his devotion and love for his ailing mother. He had known all along that it would be less expensive and burdensome for him for Bertha to be placed in a facility. However, he feared nursing home admission represented giving up and failing to fulfill my duty to my mother. Tears ran down the face of this otherwise well composed, immaculately dressed, articulate man in his late forties. He had assumed the responsibility for his mother's care while still a child and had carried this self‐imposed moral burden his entire life. This meeting was his first opportunity to voice explicitly to the medical team his immense love and concern for his dear mother.

I gently probed to clarify Bertha's values and goals. On a brief, prior nursing home stay, Bertha had found the experience to be scary and unfamiliar. However, as her functional abilities continued to decline, her feelings had changed. She now felt lonely and anxious at home when her caretakers were absent. She actually wanted to go to a nursing home, where there would always be company and support available! She had not told Douglas this because she knew he cared for her deeply and she didn't want to hurt his feelings; he seemed committed to caring for her the same way he had for so many years.

In short, Douglas knew it would be easier for him if his mother were in a nursing facility, but assumed she wanted to stay in her home. Oddly, Bertha was only remaining at home because she believed that was what her son wished. A few days later, Bertha was transferred to a nursing home near several relatives who would visit her regularly. Douglas was again selfless in not seeking to move her closer to him. He didn't want to uproot her more than was unavoidable.

Day‐to‐day practice reveals many examples of love and dedication, but I have never seen such blinding and unquestioning commitment as exemplified by this mother‐son duo. From them, I learned the importance of attentive and active listening. Our polite patients may only subtly hint at matters of the deepest import. If we cannot truly hear their unspoken emotions, we risk harming them and misinterpreting their words and actions. Some healthcare providers had seen Douglas as aggressive and demanding with his threat of a lawsuit, whereas Bertha had been described as unrealistic or in denial. These views distorted a much more complex reality. Time and attention to careful communication between the healthcare providers, the patient, and her son bore fruit in this case. The procedure that was really needed was the family meeting and not the tracheostomy! An undesired and invasive procedure was avoided, goals of care were clarified, quality of life maximized, a safe discharge arranged, and a new mutual understanding achieved. I was humbled, and reminded of the importance of team‐based care and the need to approach each patient and family member in a receptive, nonjudgmental, and open manner. Douglas and Bertha Johnson were linked with a profound and abiding bond that would only be severed at death.

Bertha Johnson is back with pneumonia again. The ED doctor on the telephone sounded both matter‐of‐fact and mildly bored when I answered her page about another admission to the hospitalist service. I hadn't met Mrs. Johnson previously, but came to know her and Douglas, her only son, well over the next few days.

The Johnsons were facing a difficult choice. Bertha was now bedbound and quadriplegic following a 40‐year battle with multiple sclerosis and gradually mounting disability. She was cognitively intact and had a solid grasp of medical realities, but was hard of hearing and quite dysarthric. Forming even short phrases and sentences took great effort. However tenuous, this ability to speak allowed her to communicate with those she loved. She had been admitted thrice in the past year with aspiration pneumonia, as she was unable to clear her secretions reliably. Repeated bronchoscopies demonstrated an inability to protect her airway. Douglas, who was also her health care proxy, favored proceeding with the tracheostomy suggested by the pulmonary team. On a prior admission, he had been distressed when his mother refused this intervention. Now she was back with the identical problem and had been given the same recommendation by her doctors. It was particularly difficult for me to discuss these sensitive issues when I had not previously met either Bertha or her son. I spoke to her primary care physician over the phone and he agreed with the need for tracheostomy. The pulmonary team had been involved in discussions about tracheostomy in all of her hospital admissions, providing continuity of care in the process. Ultimately, it was my responsibility to help Bertha and Douglas come to a decision.

After multiple discussions between the Johnsons and me, a consensus emerged to proceed with the tracheostomy. We recognized that the procedure would increase her care needs and arranged for a stay in a skilled nursing facility to provide access to round‐the‐clock suctioning. The evening prior to the tracheostomy, the floor nurse and I reviewed the procedure to ensure that Bertha was fully prepared. What followed resulted in a drastic change of plan. Bertha emphasized that she did not want to lose her only means of communication, even if the surgery would prolong her life. She admitted that she reluctantly agreed to the procedure only to please her son and doctors, because they believed it to be in her best interest. Her fear of the prospect of death from drowning in her own secretions was much less than her fear of silence and isolation that would result from her loss of speech. I shared her misgivings with Douglas, and she admitted to him that she had only agreed to the procedure for his sake. We cancelled the surgery.

Douglas later revealed that he also had been ambivalent about the procedure for sometime, as it would necessitate a nursing home stay and the loss of the caretakers who had cared for his mother so wonderfully for many years. Bertha lived alone in her own home with the help of visiting nurses and patient care assistants Douglas paid for out‐of‐pocket. Douglas lived and worked in a city over a hundred miles away, but managed to visit several times a month to facilitate his mother's care. He had supported the procedure only because the doctors had said it was the only way to avoid future pneumonias. The idea of a tracheostomy was definitively abandoned once and for all.

The Johnsons wanted Bertha to return to her home, but hospital case managers felt this would be an unsafe plan of care as she was alone for several hours a day. She was largely immobile and unable to escape if there were a fire or other emergency. Also, the caretakers were not trained to use the suction equipment, and the visiting nurses would only be available intermittently. The home care staff felt they could no longer meet her needs and declined to resume her care. Douglas became very frustrated by the delays and protracted negotiations, enough so that he threatened to sue the institution for taking over my mother's life. The threat of litigation is usually a cry for help that reflects either miscommunication or the suffering of a conflicted family.

As their hospitalist, I hoped to advocate for both the patient and Douglas while coordinating the overall care plan. I had always received consistent responses from the Johnsons, but other staff members noted that Douglas had expressed shifting views on the best site of care for his mother. At Bertha's request, I convened a meeting with her, Douglas, the social worker, case manager, visiting and staff nurses, the palliative care nurse, and floor manager. Prior to this, I met with all involved health care providers to ensure we understood each other's abilities and limitations regarding Bertha's care. As I entered the room for the family meeting, I knew it was ultimately the patient's choice whether she wanted to return home or notas long as she understood the risks involved.

During the meeting, all the team members explained the dilemmas they faced in planning for a safe disposition. Douglas's response illuminated his devotion and love for his ailing mother. He had known all along that it would be less expensive and burdensome for him for Bertha to be placed in a facility. However, he feared nursing home admission represented giving up and failing to fulfill my duty to my mother. Tears ran down the face of this otherwise well composed, immaculately dressed, articulate man in his late forties. He had assumed the responsibility for his mother's care while still a child and had carried this self‐imposed moral burden his entire life. This meeting was his first opportunity to voice explicitly to the medical team his immense love and concern for his dear mother.

I gently probed to clarify Bertha's values and goals. On a brief, prior nursing home stay, Bertha had found the experience to be scary and unfamiliar. However, as her functional abilities continued to decline, her feelings had changed. She now felt lonely and anxious at home when her caretakers were absent. She actually wanted to go to a nursing home, where there would always be company and support available! She had not told Douglas this because she knew he cared for her deeply and she didn't want to hurt his feelings; he seemed committed to caring for her the same way he had for so many years.

In short, Douglas knew it would be easier for him if his mother were in a nursing facility, but assumed she wanted to stay in her home. Oddly, Bertha was only remaining at home because she believed that was what her son wished. A few days later, Bertha was transferred to a nursing home near several relatives who would visit her regularly. Douglas was again selfless in not seeking to move her closer to him. He didn't want to uproot her more than was unavoidable.

Day‐to‐day practice reveals many examples of love and dedication, but I have never seen such blinding and unquestioning commitment as exemplified by this mother‐son duo. From them, I learned the importance of attentive and active listening. Our polite patients may only subtly hint at matters of the deepest import. If we cannot truly hear their unspoken emotions, we risk harming them and misinterpreting their words and actions. Some healthcare providers had seen Douglas as aggressive and demanding with his threat of a lawsuit, whereas Bertha had been described as unrealistic or in denial. These views distorted a much more complex reality. Time and attention to careful communication between the healthcare providers, the patient, and her son bore fruit in this case. The procedure that was really needed was the family meeting and not the tracheostomy! An undesired and invasive procedure was avoided, goals of care were clarified, quality of life maximized, a safe discharge arranged, and a new mutual understanding achieved. I was humbled, and reminded of the importance of team‐based care and the need to approach each patient and family member in a receptive, nonjudgmental, and open manner. Douglas and Bertha Johnson were linked with a profound and abiding bond that would only be severed at death.

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Inappropriate Hospital Prescribing

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Potentially inappropriate medication use in hospitalized elders

Medications can be considered inappropriate when their risk outweighs their benefit. The Beers list1 identifies medications that should be avoided in persons 65 years or older because they are ineffective or pose an unnecessarily high risk or because a safer alternative is available. Initially developed in 1991, the list has gained wide acceptance and has been updated twice.2, 3 In July 1999 it was adopted by the Centers for Medicare & Medicaid Services (CMS) for nursing home regulation, and in 2006 the National Committee on Quality Assurance adopted a modified version as a Health Plan Employer Data and Information Set (HEDIS) measure of quality of care for older Americans.4

A number of studies have demonstrated that inappropriate prescribing is common in the ambulatory setting,57 in nursing homes,8, 9 and in emergency departments10, 11 and that exposure to inappropriate medications is associated with increased risk of adverse drug reactions12 and hospitalization.13, 14 Initial studies of hospitalized patients1517 suggest that potentially inappropriate prescribing is also common among elderly inpatients and that reducing the misuse of psychotropic medications can prevent falls.18 We report on the incidence of and risk factors associated with potentially inappropriate prescribing in a large sample of hospitalized elders.

METHODS

Patients

We conducted a retrospective cohort study using data from 384 hospitals participating in Perspective (Premier, Inc., Charlotte, NC), a database developed for measuring quality and health care utilization. Participating hospitals represent all regions of the United States and are primarily small‐ to medium‐sized nonteaching hospitals most of which are in urban areas. Premier collects data elements from participating hospitals via a custom data extract from hospitals' decision support system. Hospitals aggregate the data elements into their decision support systems from multiple information technology systems including billing, medical records, pharmacy, and laboratory systems. In addition to the information contained in the standard hospital discharge file, Perspective includes a date‐stamped log of all billed items, including medications with dose and quantity, for individual patients.

We included patients at least 65 years old admitted between September 1, 2002, and June 30, 2005, with a principal diagnosis of acute myocardial infarction, chronic obstructive pulmonary disease, chest pain, community‐acquired pneumonia, congestive heart failure, ischemic stroke, or urinary tract infection. International Classification of Diseases, Ninth Revision (ICD‐9‐CM) codes were used to identify diagnoses. Patients cared for by an attending physician with a surgical specialty were excluded. The study protocol was approved by the institutional review board of Baystate Medical Center.

Data Elements

For each patient, Perspective contains fields for age, sex, race, marital status, insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9‐CM secondary diagnosis codes and APR‐DRGs using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser.19 Because almost all patients had Medicare coverage, plans were classified according to managed care status. Finally, for each patient we identified all medications administered, as well as discharge status, readmission rate, total costs, and length of stay. Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban or rural), teaching status, and whether there were geriatricians.

Potentially Inappropriate Prescribing

Using the 2002 updated Beers criteria3 for potentially inappropriate medication (PIM) use in older adults, we identified the total number of PIMs administered to each patient during his or her hospital stay. We classified each PIM as either high or low severity based on the expert consensus expressed in the 1997 update of the Beers criteria.2 The list contains 48 PIMs and an additional 20 that should be avoided in patients with certain conditions. We did not include the second category of PIMs because we did not necessarily have sufficient patient information to make this determination. In addition, some of the standard PIMs, such as laxatives, although inappropriate for chronic outpatient use, could be appropriate in the hospital setting and were excluded from this analysis. Finally, several medications were considered inappropriate only above a given threshold (eg, lorazepam >3.0 mg/day) or for patients without a specific diagnosis (eg, digoxin >0.125 mg/day for patients without atrial fibrillation). We grouped PIMs that had similar side effects into 4 categories: sedatives, anticholinergics, causing orthostasis, or causing bleeding (Fig. 1).

Figure 1
Beers list of potentially inappropriate medications modified for hospitalized patients >65 years old.

Statistical Analysis

Summary statistics at the patient, physician, and hospital levels were constructed using frequencies and proportions for categorical data and means, standard deviations, medians, interquartile ranges, and box plots for continuous‐scale variables. Patients were identified as receiving a PIM if the drug was administered (above threshold dose where applicable) on at least 1 hospital day. We examined the association of each patient characteristic with use of any PIM, any high‐severity‐rated PIM, and each side effect category using chi‐square statistics. Kruskal‐Wallis analysis of variance was used to examine variation in hospital use rates by each hospital characteristic, and physician use rates for high‐severity PIMs by attending specialty. To examine whether it was feasible to avoid PIMs altogether, we compared individual hospitals as well as individual prescribers within their specialty, limiting the comparison to hospitals that contributed at least 100 patients and to physicians with at least 50 patients.

We developed a multivariable model for any high‐severity medication (HS‐PIM) use that included all patient, physician, and hospital characteristics except length of stay, mortality, cost, discharge status, and readmission rate. A generalized estimating equation model (SAS PROC GENMOD) with a logit link and a subcluster correlation structure was used to account for clustering at the hospital, physician, and diagnosis levels, adjusting for the clustering of primary diagnosis within physician level, nested within hospital level. Effects with P < .10 were retained in the model, and interaction effects were also evaluated for significance. Model fit was assessed using deviance and Pearson chi‐square statistics. All analyses were performed with SAS statistical software, version 9.1 (SAS Institute, Cary, NC).

RESULTS

We identified 519,853 patients at least 65 years old during the study period; 564 were excluded because of missing data for key variables or unclear principal diagnosis. An additional 25,318 were excluded because they were cared for by an attending with a surgical specialty. A total of 493,971 patients were included in the study (Table 1). Mean age was 78 years, and 24% of patients were 85 years or older. Forty‐three percent were male, 71% were white, and 39% were currently married. The most common principal diagnoses were community‐acquired pneumonia, congestive heart failure, and acute myocardial infarction. The most common comorbidities were hypertension, diabetes, and chronic pulmonary disease. Medicare was the primary payer for 91% of subjects, and 13% were in managed care plans. Most patients were cared for by internists (49%), family practitioners (18%), or cardiologists (17%). Only 1% of patients had a geriatrician as attending.

Characteristics of 493,971 Older Patients Hospitalized with 1 of 7 Common Medical Conditions
Characteristicn (%)
  • Other physician category includes 42 specialties.

Age group 
6574 years168,527 (34%)
7584 years206,407 (42%)
85+ years119,037 (24%)
Sex 
Male212,358 (43%)
Female281,613 (57%)
Race 
White351,331 (71%)
Black52,429 (11%)
Hispanic18,057 (4%)
American Indian1876 (0%)
Asian/Pacific Islander5926 (1%)
Other64,352 (13%)
Marital status 
Married/partner194,496 (39%)
Widowed155,273 (31%)
Single/separated/divorced75,964 (15%)
Other68,238 (14%)
Primary diagnosis 
Pneumonia122,732 (25%)
Heart failure109,071 (22%)
Acute MI70,581 (14%)
Ischemic stroke57,204 (12%)
Chest pain50,404 (10%)
COPD44,582 (9%)
Urinary tract infection39,397 (8%)
Comorbidities 
Hypertension310,163 (63%)
Diabetes151,755 (31%)
Chronic pulmonary disease134,900 (27%)
Fluid and electrolyte disorders128,703 (26%)
Deficiency anemias92,668 (19%)
Congestive heart failure69,201 (14%)
Hypothyroidism68,711 (14%)
Peripheral vascular disease47,244 (10%)
Depression41,507 (8%)
Other neurological disorders40,200 (8%)
Renal failure38,134 (8%)
Obesity25,143 (5%)
Payer type 
Not Managed care431,583 (87%)
Managed care62,388 (13%)
Attending physician specialty 
Internal medicine (internist)241,982 (49%)
Family/general medicine90,827 (18%)
Cardiology83,317 (17%)
Pulmonology21,163 (4%)
Hospitalist14,924 (3%)
Nephrology8257 (2%)
Neurology5800 (1%)
Geriatrics3099 (1%)
Other*24,602 (5%)
 
Mortality 
Expired28,321 (6%)
Alive465,650 (94%)
Discharge status, n (% of survivors) 
Home323,629 (66%)
Nursing care119,468 (24%)
Transfer/short‐term hospital13,531 (3%)
Hospice9022 (2%)
14‐Day readmission, n (% of survivors) 
Yes35,309 (8%)
No430,334 (92%)
Length of stay (days), median (IQR)4 (2, 7)
Total cost (dollars)$5513 ($3366, $9902)

Just under half of all patients (49%) received at least 1 PIM, and 6% received 3 or more (Table 2). Thirty‐eight percent received at least 1 drug with a high severity rating (HS‐PIM). The most common PIMs were promethazine, diphenhydramine, propoxyphene, clonidine, amiodarone, and lorazepam (>3 mg/day).

Number and Type of Potentially Inappropriate Medications (PIMs) Prescribed
 Patients, n (%)
Number of PIMs 
0254,200 (51%)
1146,028 (30%)
261,445 (12%)
322,128 (4%)
4+10,170 (2%)
Number of high‐severity‐rated PIMs 
0304,523 (62%)
1129,588 (26%)
243,739 (9%)
312,213 (2%)
4+3908 (1%)
Use of any PIM by side effect class 
Sedatives156,384 (32%)
Anticholinergic effects109,293 (22%)
Causing orthostasis43,805 (9%)
Causing bleeding14,744 (3%)
Most commonly prescribed 
Promethazine49,888 (10%)
Diphenhydramine45,458 (9%)
Propoxyphene41,786 (8%)
Clonidine34,765 (7%)
Amiodarone34,318 (7%)
Lorazepam (>3 mg/day)25,147 (5%)

Patient, Physician, and Hospital Factors Associated with PIMs

Patient, physician, and hospital characteristics were all associated with use of PIMs (Table 3). In univariate analyses, older patients were less likely to receive any class of PIM, and this difference was accentuated for HS‐PIMs. Women, American Indians, married people, and those not in managed care plans were slightly more likely to receive PIMs, whereas patients admitted with acute myocardial infarction or congestive heart failure were even more likely to receive PIMs (P < .0001 for all comparisons).

Potentially Inappropriate Medication Use by Patient Characteristic
Patient characteristicAny PIM n (row %)Any high‐severity PIM n (row %)Sedatives n (row %)Anticholinergic effects n (row %)Causing orthostasis n (row %)Causing bleeding n (row %)
  • An additional 24,602 patients (5%) seen by 42 additional physician specialties were excluded from this analysis.

  • Chi‐square tests indicated all differences by patient characteristics were significant at P < .0001 except there was no significant difference by managed care status for use rates of drugs potentially causing orthostasis or drugs potentially causing bleeding.

Overall239,771 (49%)189,448 (38%)156,384 (32%)109,293 (22%)43,805 (9%)14,744 (3%)
Age group
6574 years89,168 (53%)72,573 (43%)61,399 (36%)44,792 (27%)15,799 (9%)6655 (4%)
7584 years100,787 (49%)79,595 (39%)65,034 (32%)45,121 (22%)18,519 (9%)5727 (3%)
85+ years49,816 (42%)37,280 (31%)29,951 (25%)19,380 (16%)9487 (8%)2362 (2%)
Sex
Male100,824 (47%)79,535 (37%)63,591 (30%)42,754 (20%)17,885 (8%)5771 (3%)
Female138,947 (49%)109,913 (39%)92,793 (33%)66,539 (24%)25,920 (9%)8973 (3%)
Race
White173,481 (49%)139,941 (40%)112,556 (32%)81,097 (23%)27,555 (8%)10,590 (3%)
Black26,793 (51%)18,655 (36%)18,720 (36%)11,263 (21%)8925 (17%)1536 (3%)
Hispanic8509 (47%)6370 (35%)5549 (31%)3505 (19%)2047 (11%)648 (4%)
American Indian1091 (58%)849 (45%)818 (44%)563 (30%)190 (10%)76 (4%)
Asian/Pacific Islander2386 (40%)1896 (32%)1420 (24%)1023 (17%)519 (9%)127 (2%)
Other27,511 (43%)21,737 (34%)17,321 (27%)11,842 (18%)4569 (7%)1767 (3%)
Marital status
Married/partner96,874 (50%)77,803 (40%)63,303 (33%)45,042 (23%)16,765 (9%)5969 (3%)
Widowed74,622 (48%)58,012 (37%)48,367 (31%)33,516 (22%)13,865 (9%)4354 (3%)
Single/separated/divorced36,583 (48%)28,799 (38%)24,251 (32%)16,115 (21%)7229 (10%)2399 (3%)
Other31,692 (46%)24,834 (36%)20,463 (30%)14,620 (21%)5946 (9%)2022 (3%)
Primary diagnosis
Pneumonia56,845 (46%)46,271 (38%)35,353 (29%)25,484 (21%)9184 (7%)4155 (3%)
Heart failure56,460 (52%)42,231 (39%)34,340 (31%)22,093 (20%)10,117 (9%)1945 (2%)
Acute MI43,046 (61%)37,849 (54%)32,560 (46%)25,568 (36%)4738 (7%)2549 (4%)
Ischemic stroke25,763 (45%)17,613 (31%)18,500 (32%)8742 (15%)9644 (17%)1384 (2%)
Chest pain20,655 (41%)16,363 (32%)13,536 (27%)10,520 (21%)3474 (7%)2027 (4%)
COPD18,876 (42%)14,626 (33%)12,087 (27%)8096 (18%)3209 (7%)1139 (3%)
Urinary tract infection18,126 (46%)14,495 (37%)10,008 (25%)8790 (22%)3439 (9%)1545 (4%)
Payer type
Nonmanaged care212,150 (49%)168,013 (39%)138,679 (32%)97,776 (23%)38,341 (9%)12,868 (3%)
Managed care27,621 (44%)21,435 (34%)17,705 (28%)11,517 (18%)5464 (9%)1876 (3%)
Attending physician specialty*
Internal medicine (internist%)112,664 (47%)86,907 (36%)71,382 (30%)48,746 (20%)23,221 (10%)7086 (3%)
Family/general medicine41,303 (45%)32,338 (36%)25,653 (28%)18,274 (20%)7660 (8%)2852 (3%)
Cardiology48,485 (58%)40,752 (49%)34,859 (42%)25,792 (31%)5455 (7%)2542 (3%)
Pulmonology10,231 (48%)8105 (38%)6746 (32%)4064 (19%)1739 (8%)574 (3%)
Hospitalist7003 (47%)5443 (36%)4447 (30%)3179 (21%)1471 (10%)463 (3%)
Nephrology4508 (55%)3388 (41%)3132 (38%)2054 (25%)1326 (16%)198 (2%)
Neurology2420 (42%)1789 (31%)1625 (28%)851 (15%)699 (12%)174 (3%)
Geriatrics1020 (33%)785 (25%)596 (19%)404 (13%)196 (6%)41 (1%)

The HS‐PIM prescribing varied substantially by attending specialty (Fig. 2). Internists, family practitioners, and hospitalists all had similar median rates (33%), cardiologists had a higher median rate (48%), and geriatricians had a lower rate (24%). The most common PIM also differed by specialty: whereas promethazine was the most commonly prescribed drug across most specialties, nephrologists and neurologists used clonidine, pulmonologists used lorazepam, and cardiologists used diphenhydramine most often. Among the 8% of physicians who saw at least 50 patients, there was also great variation in each specialty (Fig. 2). Among internists and cardiologists who saw at least 50 patients, the high‐severity PIM usage rate ranged from 0% to more than 90%.

Figure 2
Distribution of physician prescribing rates for high‐severity‐rated PIM by specialty for physicians treating at least 50 hospitalized patients during the study period (N, number of physicians in each specialty). The line in the middle of each box represents the median use rate across physicians, and the box extends to the interquartile range (IQR). The lines emerging from the box extend to the adjacent values. The upper adjacent value is defined as the largest data point less than or equal to the 75th percentile plus 1.5 times the IQR; the lower adjacent value is defined as the smallest data point greater than or equal to the 25th percentile minus 1.5 IQR. Observed points more extreme than the adjacent values are individually plotted.

There was substantial variation in PIM usage among hospitals, most notably by region. The mean proportion of patients receiving PIMs ranged from 34% at hospitals in the Northeast to 55% at hospitals in the South (Table 4). Smaller hospitals and those in urban settings had slightly lower rates, as did those that had geriatricians on staff. The teaching status of the hospital had little effect. Variation at the individual hospital level was extreme (Fig. 3). Although half of all hospitals had rates between 43% and 58%, in 7 hospitals with more than 300 encounters each, PIMs were never prescribed for geriatric patients.

Figure 3
Distribution of hospital use rates for each category of PIM among hospitals with at least 100 eligible patients during the study period. Thirteen hospitals (3%) and 324 patients (<0.1%) were excluded. For interpretation of the box plots, see Figure 2.
Percentage of Patients Prescribed Potentially Inappropriate Medication (PIM) by Hospital Characteristic
 Hospitals Total = 384 n (%)Patients N = 49,3971 n (%)Any PIM Mean = 48.2 Mean (SD)Any high‐severity PIM Mean = 38.7 Mean (SD)Sedatives Mean = 30.2 Mean (SD)Anticholinergic effects Mean = 21.5 Mean (SD)Causing orthostasis Mean = 8.5 Mean (SD)Causing bleeding Mean = 3.1 Mean (SD)
  • Note: P values from analysis of variance of hospital use rates for each hospital characteristic.

  • P < .05,

  • P < .001,

  • P < .0001.

Hospital region  *****************
Midwest76 (20%)95,791 (19%)38.8 (19.7)30.0 (16.4)24.3 (13.8)15.1 (9.9)6.9 (6.3)3.1 (2.3)
Northeast47 (12%)79,138 (16%)34.1 (12.6)26.2 (11.2)19.0 (9.2)13.5 (8.1)4.9 (2.3)2.1 (1.6)
South199 (52%)260,870 (53%)54.5 (10.1)42.7 (9.6)36.0 (10.8)26.4 (8.6)10.4 (4.6)3.6 (2.5)
West62 (16%)58,172 (12%)45.8 (8.1)37.4 (7.1)27.3 (7.7)19.5 (5.7)7.4 (4.8)2.7 (1.3)
Teaching status        
Nonteaching297 (77%)324,948 (66%)47.3 (14.6)36.9 (12.3)29.8 (12.0)21.3 (9.9)8.7 (5.4)3.3 (2.4)
Teaching87 (23%)169,023 (34%)48.2 (16.0)38.8 (14.2)31.6 (14.5)22.1 (10.2)7.8 (4.4)2.7 (1.5)
Staffed beds   ***   
22200143 (37%)80,741 (16%)45.5 (16.9)35.2 (14.6)27.5 (14.0)20.1 (10.3)8.0 (6.2)3.5 (3.1)
200400137 (36%)177,286 (36%)47.7 (14.2)37.8 (12.0)30.5 (11.6)22.0 (10.0)8.4 (4.7)3.0 (1.6)
400+104 (27%)235944 (48%)50.1 (12.4)39.6 (10.6)33.5 (10.9)22.7 (9.3)9.3 (4.2)2.9 (1.4)
Population serviced     ****
Rural119 (31%)102,799 (21%)48.4 (13.0)38.3 (10.6)29.2 (11.0)23.2 (9.3)7.5 (4.0)3.7 (3.0)
Urban265 (69%)391,172 (79%)47.1 (15.7)36.9 (13.7)30.6 (13.2)20.7 (10.2)9.0 (5.6)2.9 (1.8)
Geriatrician presence        
No340 (89%)409,281 (83%)47.7 (15.3)37.6 (13.0)30.3 (12.8)21.7 (10.0)8.4 (5.3)3.2 (2.3)
Yes44 (11%)84,690 (17%)45.8 (11.4)35.5 (10.6)29.4 (10.8)19.6 (9.4)9.3 (4.3)2.9 (1.6)

Multivariable Model

In a multivariable logit model that included all patient, hospital, and physician characteristics and that accounted for clustering at the hospital, physician, and diagnosis levels, several characteristics were associated with HS‐PIM prescribing (Table 5). By far the most important predictor of use was hospital region. Compared with patients at hospitals in the Midwest, patients in the South (OR 1.63, 95% CI 1.591.67) and West (OR 1.43, 95% CI 1.381.47) were more likely and those in the Northeast were less likely (OR 0.85, 95% CI 0.830.88) to receive HS‐PIMs. Larger hospitals had higher HS‐PIM rates than smaller ones, but teaching status and rural or urban setting were not associated with HS‐PIM prescribing. The presence of geriatricians in a hospital was also associated with lower HS‐PIM prescribing for the entire hospital.

Adjusted Odds Ratio Estimates for High‐Severity PIM Use
Effect (reference)Odds ratio95% Confidence limits
Age   
6574 years1.00  
7584 years0.830.820.84
85+ years0.590.580.61
Sex   
Female1.00  
Male0.850.830.86
Race   
White1.00  
Black0.780.760.80
Hispanic0.840.810.87
American Indian0.970.881.07
Asian/Pacific Islander0.740.700.79
Other0.940.920.97
Marital Status   
Married/partner1.00  
Single/separated/divorced0.960.940.98
Widowed0.960.950.98
Other0.930.900.95
Primary diagnosis   
Pneumonia1.00  
COPD0.830.810.85
Heart failure1.141.121.16
Ischemic stroke0.840.820.86
Acute MI1.951.902.01
Urinary tract infection1.061.031.09
Chest pain0.870.840.89
Comorbidities (yes or no)   
Hypertension0.980.970.99
Diabetes0.980.971.00
Chronic lung disease1.111.101.13
Fluid and electrolyte disorders1.261.241.27
Anemia deficiencies1.171.151.18
Congestive heart failure1.341.321.37
Hypothyroidism1.131.111.15
Peripheral vascular disease1.091.061.11
Depression1.381.351.41
Neurological disorders0.890.870.91
Renal failure1.231.201.26
Obesity1.111.081.14
Payer type   
Managed care1.00  
Not managed care1.041.021.06
Attending physician specialty   
Internal medicine1.00  
Cardiology1.321.281.36
Family/general medicine0.990.971.01
Geriatrics0.690.610.78
Hospitalist0.900.840.96
Nephrology1.020.961.08
Neurology0.930.861.00
Pulmonology1.101.051.15
Setting   
Rural1.00  
Urban1.021.001.05
 
Teaching status   
Nonteaching1.00  
Teaching1.010.981.03
Number of beds   
222001.00  
2004001.081.051.11
400+1.121.091.16
Region   
Midwest1.00  
Northeast0.850.830.88
South1.631.591.67
West1.431.381.47
Geriatrician presence   
No1.00  
Yes0.930.900.95

Physician specialty was also important. Adjusting for diagnosis attenuated some of this association, but compared with internists, cardiologists (OR 1.32, 95% CI 1.281.36) and pulmonologists (OR 1.10, 95% CI 1.051.15) were still more likely, hospitalists (OR 0.90, 95% CI 0.840.96) were less likely, and geriatricians (0.69, 95% CI 0.610.78) were least likely to prescribe any HS‐PIM.

Patient factors were also associated with HS‐PIM use. Compared with patients age 6574 years, patients older than 85 years were much less likely to receive an HS‐PIM (OR 0.59, CI 0.580.61), as to a lesser extent were nonwhites compared with whites and unmarried people compared with those who were married. Compared with patients with pneumonia, those with COPD, stroke, or chest pain were less likely and those with myocardial infarction and congestive heart failure were more likely to receive HS‐PIMs. Patients with a secondary diagnosis of depression were also at high risk (OR 1.38, CI 1.351.41).

DISCUSSION

Although Americans age 65 years and older make up less than 15% of the U.S. population, they consume about one third of all prescription drugs20 and account for one third of all hospital admissions.21 Using the Beers list, numerous studies have documented high rates of potentially inappropriate prescribing for community‐dwelling elderly and nursing home patients and, in some studies, an attendant risk of falling,2224 hip fracture,25, 26 hospitalization,13 or death.14 Applying these same criteria to a large sample of medical inpatients, we found that almost half received a potentially inappropriate drug, most of high severity. Moreover, the PIM prescribing rate varied substantially by region, hospital, and attending physician specialty. Although the use of PIMs was associated with patient age, comorbidities, and primary diagnosis, these patient factors explained only a small portion of the variation in prescribing practices across groups of physicians and hospitals.

Using consensus criteria, Beers originally found that 40% of the residents in 12 nursing homes received at least 1 PIM,8 and studies of community‐dwelling elderly demonstrated rates of 21% to 37%, with little change over time.6, 27, 28 Several small studies have examined inpatient prescribing.16, 17, 29, 30 The largest17 found that only 15% of elderly Italian inpatients received a PIM. Our finding, that 49% of inpatients had received at least 1 PIM, may partially reflect the high prevalence of use among elderly US patients in nursing homes and the community.

Regional variation has been demonstrated for ambulatory patients in the US6 and Europe.31 Zhan et al. found slightly higher rates of PIM use in the Midwest and the South (23%) than in the Northeast and the West (19%). Variation in Europe was greater, with 41% of patients in the Czech Republic versus 5.8% of patients in Denmark receiving at least 1 PIM. We found that region was the strongest predictor of in‐hospital HS‐PIM use, with patients in the South most likely and patients in the Northeast least likely to receive HS‐PIMs. This variation persisted even after adjusting for differences in other patient and hospital factors, suggesting that local custom played a large role in the decision to prescribe HS‐PIMs. Moreover, because outpatient rates are more uniform, these large differences seem limited to inpatient practice.

Patient factors have also been examined. Advanced age was associated with decreased PIM use in some studies17, 28, 31 but not in others.6, 27 We found increasing age to be strongly associated with decreased PIM use, suggesting that in the hospital, at least, doctors take care to avoid prescribing certain drugs to the frail elderly. Women appear to be consistently at higher risk than men,6, 27, 28, 31 and white patients are more at risk than those of other races.6 Our finding that certain diagnoses were associated with higher or lower rates has not been reported previously. The lower rates associated with stroke and COPD suggest that prescribers were aware that these patients were at increased risk of delirium and respiratory depression. The higher rates associated with myocardial infarction may have to do with the use of standardized order sets (eg, cath lab orders) that do not consider the age of the patient going for the procedure.

Admission to a geriatric service32 and intervention by a clinical pharmacist33 have been shown to decrease PIM prescribing at discharge. We noted that patients cared for by a geriatrician had the lowest rates of PIM prescribing during hospitalization as well and that hospitals with geriatricians had lower rates overall, possibly demonstrating that geriatricians had a ripple effect on their colleagues. Hospitalists also had lower rates than internists, supporting the notion that hospitalists provide higher‐quality inpatient care.

Our study had some important limitations. First, we only had access to inpatient administrative records. Thus, we could not identify which medications were continued from home and which were begun in the hospital, nor could we know the indications for which specific drugs were prescribed or who prescribed them. Based on published outpatient rates, however, we could assume that many of the drugs were started in the hospital and that others could have been discontinued but were not. Second, the Beers list was developed by the modified Delphi method; there was little empirical evidence of the danger of specific drugs, although some classes, such as benzodiazepines, opiates and digoxin, have been associated with inpatient falls.18, 3436 Furthermore, our administrative database did not allow us to balance the risks and benefits for particular patients; hence, the medications were only potentially inappropriate, and our study did not address the consequences of such prescribing. Although some of these drugs may be appropriate under certain circumstances, it is unlikely that these circumstances would vary by 60% across geographic regions or that internists would encounter these circumstances more often than do hospitalists. Thus, although we could not identify specific patients who received inappropriate medications, we did identify certain hospitals and even whole regions of the country in which the rate of inappropriate prescribing was high. Third, the Beers list, which was developed for outpatient use, may be less relevant in the inpatient setting. However, given that inpatients have more organ dysfunction and are at higher risk of delirium and falls, it may actually be more applicable to hospitalized patients. We similarly did not distinguish between single and multiple doses because the Beers list does not make such a distinction, and there is no empirical evidence that a single dose is safe. Indeed, patients are often at highest risk of falls immediately after initiation of therapy.3739 We did, however, exclude drugs such as laxatives, which may be appropriate for brief inpatient use but not for chronic use.

Our study also had a number of strengths. The large sample size, representing approximately 5% of annual inpatient admissions in the US over 2 years, offered an instructive look at the recent prescribing patterns of thousands of US physicians. We were able to identify many patient, physician, and hospital factors associated with PIM prescribing that have not previously been reported. Some of these factors, such as advanced age and comorbid diagnoses, suggest that physicians do tailor their treatment to individual patients. Nevertheless, patient factors accounted for only a small portion of the variation in prescribing. The largest variation, associated with regional, hospital, and physician factors, highlights the opportunity for improvement.

At the same time, our findings are encouraging for 2 reasons. First, most inappropriate prescribing involved only a handful of medications, so small changes in prescribing patterns could have a tremendous impact. Second, observing the practice of individual physicians and hospitals reveals what is possible. We found that in most specialties there were physicians who rarely or never used PIMs. We also found 7 hospitals, each with at least 300 cases, where no PIMs were ever prescribed.

Where should hospitals focus their efforts to prevent inappropriate prescribing? Our data highlight the complexity of the problem, which seems daunting. PIM prescribing is spread across all specialties, including geriatrics, and although cardiologists had the highest rate of prescribing, internists, who were more numerous, accounted for a much higher overall number of potentially inappropriate prescriptions. It would be instructive to study the 7 hospitals where PIMs were never prescribed or to interview those physicians who never prescribed PIMs, but the anonymous nature of our data would not allow for this. However, our data do suggest some directions. First, hospitals should become aware of their own rates of PIM use because measurement is the first step in quality improvement. Next, hospitals should focus efforts on reducing the use of the most common drugs. Eliminating just 3 drugs promethazine, diphenhydramine, and propoxyphenewould reduce the use of PIMs in 24% of elderly patients. Enlisting hospital pharmacists and electronic health records and reviewing standard order sets for elderly patients are potentially effective strategies. Finally, increasing the presence of geriatricians and hospitalists would be expected to have a modest impact.

In a representative sample of elderly inpatients, we found that almost half received a potentially inappropriate medication and that the rate of inappropriate prescribing varied widely among doctors and hospitals. Additional research is needed to distinguish which of the Beers drugs are most harmful and which patients are at highest risk. Research should also focus on understanding differences in prescribing patterns, perhaps by studying the outliers at both ends of the quality spectrum, and on techniques to minimize non‐patient‐centered variation.

References
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Article PDF
Issue
Journal of Hospital Medicine - 3(2)
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Page Number
91-102
Legacy Keywords
drug safety, geriatric patient, pharmaceuticals, quality improvement
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Article PDF

Medications can be considered inappropriate when their risk outweighs their benefit. The Beers list1 identifies medications that should be avoided in persons 65 years or older because they are ineffective or pose an unnecessarily high risk or because a safer alternative is available. Initially developed in 1991, the list has gained wide acceptance and has been updated twice.2, 3 In July 1999 it was adopted by the Centers for Medicare & Medicaid Services (CMS) for nursing home regulation, and in 2006 the National Committee on Quality Assurance adopted a modified version as a Health Plan Employer Data and Information Set (HEDIS) measure of quality of care for older Americans.4

A number of studies have demonstrated that inappropriate prescribing is common in the ambulatory setting,57 in nursing homes,8, 9 and in emergency departments10, 11 and that exposure to inappropriate medications is associated with increased risk of adverse drug reactions12 and hospitalization.13, 14 Initial studies of hospitalized patients1517 suggest that potentially inappropriate prescribing is also common among elderly inpatients and that reducing the misuse of psychotropic medications can prevent falls.18 We report on the incidence of and risk factors associated with potentially inappropriate prescribing in a large sample of hospitalized elders.

METHODS

Patients

We conducted a retrospective cohort study using data from 384 hospitals participating in Perspective (Premier, Inc., Charlotte, NC), a database developed for measuring quality and health care utilization. Participating hospitals represent all regions of the United States and are primarily small‐ to medium‐sized nonteaching hospitals most of which are in urban areas. Premier collects data elements from participating hospitals via a custom data extract from hospitals' decision support system. Hospitals aggregate the data elements into their decision support systems from multiple information technology systems including billing, medical records, pharmacy, and laboratory systems. In addition to the information contained in the standard hospital discharge file, Perspective includes a date‐stamped log of all billed items, including medications with dose and quantity, for individual patients.

We included patients at least 65 years old admitted between September 1, 2002, and June 30, 2005, with a principal diagnosis of acute myocardial infarction, chronic obstructive pulmonary disease, chest pain, community‐acquired pneumonia, congestive heart failure, ischemic stroke, or urinary tract infection. International Classification of Diseases, Ninth Revision (ICD‐9‐CM) codes were used to identify diagnoses. Patients cared for by an attending physician with a surgical specialty were excluded. The study protocol was approved by the institutional review board of Baystate Medical Center.

Data Elements

For each patient, Perspective contains fields for age, sex, race, marital status, insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9‐CM secondary diagnosis codes and APR‐DRGs using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser.19 Because almost all patients had Medicare coverage, plans were classified according to managed care status. Finally, for each patient we identified all medications administered, as well as discharge status, readmission rate, total costs, and length of stay. Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban or rural), teaching status, and whether there were geriatricians.

Potentially Inappropriate Prescribing

Using the 2002 updated Beers criteria3 for potentially inappropriate medication (PIM) use in older adults, we identified the total number of PIMs administered to each patient during his or her hospital stay. We classified each PIM as either high or low severity based on the expert consensus expressed in the 1997 update of the Beers criteria.2 The list contains 48 PIMs and an additional 20 that should be avoided in patients with certain conditions. We did not include the second category of PIMs because we did not necessarily have sufficient patient information to make this determination. In addition, some of the standard PIMs, such as laxatives, although inappropriate for chronic outpatient use, could be appropriate in the hospital setting and were excluded from this analysis. Finally, several medications were considered inappropriate only above a given threshold (eg, lorazepam >3.0 mg/day) or for patients without a specific diagnosis (eg, digoxin >0.125 mg/day for patients without atrial fibrillation). We grouped PIMs that had similar side effects into 4 categories: sedatives, anticholinergics, causing orthostasis, or causing bleeding (Fig. 1).

Figure 1
Beers list of potentially inappropriate medications modified for hospitalized patients >65 years old.

Statistical Analysis

Summary statistics at the patient, physician, and hospital levels were constructed using frequencies and proportions for categorical data and means, standard deviations, medians, interquartile ranges, and box plots for continuous‐scale variables. Patients were identified as receiving a PIM if the drug was administered (above threshold dose where applicable) on at least 1 hospital day. We examined the association of each patient characteristic with use of any PIM, any high‐severity‐rated PIM, and each side effect category using chi‐square statistics. Kruskal‐Wallis analysis of variance was used to examine variation in hospital use rates by each hospital characteristic, and physician use rates for high‐severity PIMs by attending specialty. To examine whether it was feasible to avoid PIMs altogether, we compared individual hospitals as well as individual prescribers within their specialty, limiting the comparison to hospitals that contributed at least 100 patients and to physicians with at least 50 patients.

We developed a multivariable model for any high‐severity medication (HS‐PIM) use that included all patient, physician, and hospital characteristics except length of stay, mortality, cost, discharge status, and readmission rate. A generalized estimating equation model (SAS PROC GENMOD) with a logit link and a subcluster correlation structure was used to account for clustering at the hospital, physician, and diagnosis levels, adjusting for the clustering of primary diagnosis within physician level, nested within hospital level. Effects with P < .10 were retained in the model, and interaction effects were also evaluated for significance. Model fit was assessed using deviance and Pearson chi‐square statistics. All analyses were performed with SAS statistical software, version 9.1 (SAS Institute, Cary, NC).

RESULTS

We identified 519,853 patients at least 65 years old during the study period; 564 were excluded because of missing data for key variables or unclear principal diagnosis. An additional 25,318 were excluded because they were cared for by an attending with a surgical specialty. A total of 493,971 patients were included in the study (Table 1). Mean age was 78 years, and 24% of patients were 85 years or older. Forty‐three percent were male, 71% were white, and 39% were currently married. The most common principal diagnoses were community‐acquired pneumonia, congestive heart failure, and acute myocardial infarction. The most common comorbidities were hypertension, diabetes, and chronic pulmonary disease. Medicare was the primary payer for 91% of subjects, and 13% were in managed care plans. Most patients were cared for by internists (49%), family practitioners (18%), or cardiologists (17%). Only 1% of patients had a geriatrician as attending.

Characteristics of 493,971 Older Patients Hospitalized with 1 of 7 Common Medical Conditions
Characteristicn (%)
  • Other physician category includes 42 specialties.

Age group 
6574 years168,527 (34%)
7584 years206,407 (42%)
85+ years119,037 (24%)
Sex 
Male212,358 (43%)
Female281,613 (57%)
Race 
White351,331 (71%)
Black52,429 (11%)
Hispanic18,057 (4%)
American Indian1876 (0%)
Asian/Pacific Islander5926 (1%)
Other64,352 (13%)
Marital status 
Married/partner194,496 (39%)
Widowed155,273 (31%)
Single/separated/divorced75,964 (15%)
Other68,238 (14%)
Primary diagnosis 
Pneumonia122,732 (25%)
Heart failure109,071 (22%)
Acute MI70,581 (14%)
Ischemic stroke57,204 (12%)
Chest pain50,404 (10%)
COPD44,582 (9%)
Urinary tract infection39,397 (8%)
Comorbidities 
Hypertension310,163 (63%)
Diabetes151,755 (31%)
Chronic pulmonary disease134,900 (27%)
Fluid and electrolyte disorders128,703 (26%)
Deficiency anemias92,668 (19%)
Congestive heart failure69,201 (14%)
Hypothyroidism68,711 (14%)
Peripheral vascular disease47,244 (10%)
Depression41,507 (8%)
Other neurological disorders40,200 (8%)
Renal failure38,134 (8%)
Obesity25,143 (5%)
Payer type 
Not Managed care431,583 (87%)
Managed care62,388 (13%)
Attending physician specialty 
Internal medicine (internist)241,982 (49%)
Family/general medicine90,827 (18%)
Cardiology83,317 (17%)
Pulmonology21,163 (4%)
Hospitalist14,924 (3%)
Nephrology8257 (2%)
Neurology5800 (1%)
Geriatrics3099 (1%)
Other*24,602 (5%)
 
Mortality 
Expired28,321 (6%)
Alive465,650 (94%)
Discharge status, n (% of survivors) 
Home323,629 (66%)
Nursing care119,468 (24%)
Transfer/short‐term hospital13,531 (3%)
Hospice9022 (2%)
14‐Day readmission, n (% of survivors) 
Yes35,309 (8%)
No430,334 (92%)
Length of stay (days), median (IQR)4 (2, 7)
Total cost (dollars)$5513 ($3366, $9902)

Just under half of all patients (49%) received at least 1 PIM, and 6% received 3 or more (Table 2). Thirty‐eight percent received at least 1 drug with a high severity rating (HS‐PIM). The most common PIMs were promethazine, diphenhydramine, propoxyphene, clonidine, amiodarone, and lorazepam (>3 mg/day).

Number and Type of Potentially Inappropriate Medications (PIMs) Prescribed
 Patients, n (%)
Number of PIMs 
0254,200 (51%)
1146,028 (30%)
261,445 (12%)
322,128 (4%)
4+10,170 (2%)
Number of high‐severity‐rated PIMs 
0304,523 (62%)
1129,588 (26%)
243,739 (9%)
312,213 (2%)
4+3908 (1%)
Use of any PIM by side effect class 
Sedatives156,384 (32%)
Anticholinergic effects109,293 (22%)
Causing orthostasis43,805 (9%)
Causing bleeding14,744 (3%)
Most commonly prescribed 
Promethazine49,888 (10%)
Diphenhydramine45,458 (9%)
Propoxyphene41,786 (8%)
Clonidine34,765 (7%)
Amiodarone34,318 (7%)
Lorazepam (>3 mg/day)25,147 (5%)

Patient, Physician, and Hospital Factors Associated with PIMs

Patient, physician, and hospital characteristics were all associated with use of PIMs (Table 3). In univariate analyses, older patients were less likely to receive any class of PIM, and this difference was accentuated for HS‐PIMs. Women, American Indians, married people, and those not in managed care plans were slightly more likely to receive PIMs, whereas patients admitted with acute myocardial infarction or congestive heart failure were even more likely to receive PIMs (P < .0001 for all comparisons).

Potentially Inappropriate Medication Use by Patient Characteristic
Patient characteristicAny PIM n (row %)Any high‐severity PIM n (row %)Sedatives n (row %)Anticholinergic effects n (row %)Causing orthostasis n (row %)Causing bleeding n (row %)
  • An additional 24,602 patients (5%) seen by 42 additional physician specialties were excluded from this analysis.

  • Chi‐square tests indicated all differences by patient characteristics were significant at P < .0001 except there was no significant difference by managed care status for use rates of drugs potentially causing orthostasis or drugs potentially causing bleeding.

Overall239,771 (49%)189,448 (38%)156,384 (32%)109,293 (22%)43,805 (9%)14,744 (3%)
Age group
6574 years89,168 (53%)72,573 (43%)61,399 (36%)44,792 (27%)15,799 (9%)6655 (4%)
7584 years100,787 (49%)79,595 (39%)65,034 (32%)45,121 (22%)18,519 (9%)5727 (3%)
85+ years49,816 (42%)37,280 (31%)29,951 (25%)19,380 (16%)9487 (8%)2362 (2%)
Sex
Male100,824 (47%)79,535 (37%)63,591 (30%)42,754 (20%)17,885 (8%)5771 (3%)
Female138,947 (49%)109,913 (39%)92,793 (33%)66,539 (24%)25,920 (9%)8973 (3%)
Race
White173,481 (49%)139,941 (40%)112,556 (32%)81,097 (23%)27,555 (8%)10,590 (3%)
Black26,793 (51%)18,655 (36%)18,720 (36%)11,263 (21%)8925 (17%)1536 (3%)
Hispanic8509 (47%)6370 (35%)5549 (31%)3505 (19%)2047 (11%)648 (4%)
American Indian1091 (58%)849 (45%)818 (44%)563 (30%)190 (10%)76 (4%)
Asian/Pacific Islander2386 (40%)1896 (32%)1420 (24%)1023 (17%)519 (9%)127 (2%)
Other27,511 (43%)21,737 (34%)17,321 (27%)11,842 (18%)4569 (7%)1767 (3%)
Marital status
Married/partner96,874 (50%)77,803 (40%)63,303 (33%)45,042 (23%)16,765 (9%)5969 (3%)
Widowed74,622 (48%)58,012 (37%)48,367 (31%)33,516 (22%)13,865 (9%)4354 (3%)
Single/separated/divorced36,583 (48%)28,799 (38%)24,251 (32%)16,115 (21%)7229 (10%)2399 (3%)
Other31,692 (46%)24,834 (36%)20,463 (30%)14,620 (21%)5946 (9%)2022 (3%)
Primary diagnosis
Pneumonia56,845 (46%)46,271 (38%)35,353 (29%)25,484 (21%)9184 (7%)4155 (3%)
Heart failure56,460 (52%)42,231 (39%)34,340 (31%)22,093 (20%)10,117 (9%)1945 (2%)
Acute MI43,046 (61%)37,849 (54%)32,560 (46%)25,568 (36%)4738 (7%)2549 (4%)
Ischemic stroke25,763 (45%)17,613 (31%)18,500 (32%)8742 (15%)9644 (17%)1384 (2%)
Chest pain20,655 (41%)16,363 (32%)13,536 (27%)10,520 (21%)3474 (7%)2027 (4%)
COPD18,876 (42%)14,626 (33%)12,087 (27%)8096 (18%)3209 (7%)1139 (3%)
Urinary tract infection18,126 (46%)14,495 (37%)10,008 (25%)8790 (22%)3439 (9%)1545 (4%)
Payer type
Nonmanaged care212,150 (49%)168,013 (39%)138,679 (32%)97,776 (23%)38,341 (9%)12,868 (3%)
Managed care27,621 (44%)21,435 (34%)17,705 (28%)11,517 (18%)5464 (9%)1876 (3%)
Attending physician specialty*
Internal medicine (internist%)112,664 (47%)86,907 (36%)71,382 (30%)48,746 (20%)23,221 (10%)7086 (3%)
Family/general medicine41,303 (45%)32,338 (36%)25,653 (28%)18,274 (20%)7660 (8%)2852 (3%)
Cardiology48,485 (58%)40,752 (49%)34,859 (42%)25,792 (31%)5455 (7%)2542 (3%)
Pulmonology10,231 (48%)8105 (38%)6746 (32%)4064 (19%)1739 (8%)574 (3%)
Hospitalist7003 (47%)5443 (36%)4447 (30%)3179 (21%)1471 (10%)463 (3%)
Nephrology4508 (55%)3388 (41%)3132 (38%)2054 (25%)1326 (16%)198 (2%)
Neurology2420 (42%)1789 (31%)1625 (28%)851 (15%)699 (12%)174 (3%)
Geriatrics1020 (33%)785 (25%)596 (19%)404 (13%)196 (6%)41 (1%)

The HS‐PIM prescribing varied substantially by attending specialty (Fig. 2). Internists, family practitioners, and hospitalists all had similar median rates (33%), cardiologists had a higher median rate (48%), and geriatricians had a lower rate (24%). The most common PIM also differed by specialty: whereas promethazine was the most commonly prescribed drug across most specialties, nephrologists and neurologists used clonidine, pulmonologists used lorazepam, and cardiologists used diphenhydramine most often. Among the 8% of physicians who saw at least 50 patients, there was also great variation in each specialty (Fig. 2). Among internists and cardiologists who saw at least 50 patients, the high‐severity PIM usage rate ranged from 0% to more than 90%.

Figure 2
Distribution of physician prescribing rates for high‐severity‐rated PIM by specialty for physicians treating at least 50 hospitalized patients during the study period (N, number of physicians in each specialty). The line in the middle of each box represents the median use rate across physicians, and the box extends to the interquartile range (IQR). The lines emerging from the box extend to the adjacent values. The upper adjacent value is defined as the largest data point less than or equal to the 75th percentile plus 1.5 times the IQR; the lower adjacent value is defined as the smallest data point greater than or equal to the 25th percentile minus 1.5 IQR. Observed points more extreme than the adjacent values are individually plotted.

There was substantial variation in PIM usage among hospitals, most notably by region. The mean proportion of patients receiving PIMs ranged from 34% at hospitals in the Northeast to 55% at hospitals in the South (Table 4). Smaller hospitals and those in urban settings had slightly lower rates, as did those that had geriatricians on staff. The teaching status of the hospital had little effect. Variation at the individual hospital level was extreme (Fig. 3). Although half of all hospitals had rates between 43% and 58%, in 7 hospitals with more than 300 encounters each, PIMs were never prescribed for geriatric patients.

Figure 3
Distribution of hospital use rates for each category of PIM among hospitals with at least 100 eligible patients during the study period. Thirteen hospitals (3%) and 324 patients (<0.1%) were excluded. For interpretation of the box plots, see Figure 2.
Percentage of Patients Prescribed Potentially Inappropriate Medication (PIM) by Hospital Characteristic
 Hospitals Total = 384 n (%)Patients N = 49,3971 n (%)Any PIM Mean = 48.2 Mean (SD)Any high‐severity PIM Mean = 38.7 Mean (SD)Sedatives Mean = 30.2 Mean (SD)Anticholinergic effects Mean = 21.5 Mean (SD)Causing orthostasis Mean = 8.5 Mean (SD)Causing bleeding Mean = 3.1 Mean (SD)
  • Note: P values from analysis of variance of hospital use rates for each hospital characteristic.

  • P < .05,

  • P < .001,

  • P < .0001.

Hospital region  *****************
Midwest76 (20%)95,791 (19%)38.8 (19.7)30.0 (16.4)24.3 (13.8)15.1 (9.9)6.9 (6.3)3.1 (2.3)
Northeast47 (12%)79,138 (16%)34.1 (12.6)26.2 (11.2)19.0 (9.2)13.5 (8.1)4.9 (2.3)2.1 (1.6)
South199 (52%)260,870 (53%)54.5 (10.1)42.7 (9.6)36.0 (10.8)26.4 (8.6)10.4 (4.6)3.6 (2.5)
West62 (16%)58,172 (12%)45.8 (8.1)37.4 (7.1)27.3 (7.7)19.5 (5.7)7.4 (4.8)2.7 (1.3)
Teaching status        
Nonteaching297 (77%)324,948 (66%)47.3 (14.6)36.9 (12.3)29.8 (12.0)21.3 (9.9)8.7 (5.4)3.3 (2.4)
Teaching87 (23%)169,023 (34%)48.2 (16.0)38.8 (14.2)31.6 (14.5)22.1 (10.2)7.8 (4.4)2.7 (1.5)
Staffed beds   ***   
22200143 (37%)80,741 (16%)45.5 (16.9)35.2 (14.6)27.5 (14.0)20.1 (10.3)8.0 (6.2)3.5 (3.1)
200400137 (36%)177,286 (36%)47.7 (14.2)37.8 (12.0)30.5 (11.6)22.0 (10.0)8.4 (4.7)3.0 (1.6)
400+104 (27%)235944 (48%)50.1 (12.4)39.6 (10.6)33.5 (10.9)22.7 (9.3)9.3 (4.2)2.9 (1.4)
Population serviced     ****
Rural119 (31%)102,799 (21%)48.4 (13.0)38.3 (10.6)29.2 (11.0)23.2 (9.3)7.5 (4.0)3.7 (3.0)
Urban265 (69%)391,172 (79%)47.1 (15.7)36.9 (13.7)30.6 (13.2)20.7 (10.2)9.0 (5.6)2.9 (1.8)
Geriatrician presence        
No340 (89%)409,281 (83%)47.7 (15.3)37.6 (13.0)30.3 (12.8)21.7 (10.0)8.4 (5.3)3.2 (2.3)
Yes44 (11%)84,690 (17%)45.8 (11.4)35.5 (10.6)29.4 (10.8)19.6 (9.4)9.3 (4.3)2.9 (1.6)

Multivariable Model

In a multivariable logit model that included all patient, hospital, and physician characteristics and that accounted for clustering at the hospital, physician, and diagnosis levels, several characteristics were associated with HS‐PIM prescribing (Table 5). By far the most important predictor of use was hospital region. Compared with patients at hospitals in the Midwest, patients in the South (OR 1.63, 95% CI 1.591.67) and West (OR 1.43, 95% CI 1.381.47) were more likely and those in the Northeast were less likely (OR 0.85, 95% CI 0.830.88) to receive HS‐PIMs. Larger hospitals had higher HS‐PIM rates than smaller ones, but teaching status and rural or urban setting were not associated with HS‐PIM prescribing. The presence of geriatricians in a hospital was also associated with lower HS‐PIM prescribing for the entire hospital.

Adjusted Odds Ratio Estimates for High‐Severity PIM Use
Effect (reference)Odds ratio95% Confidence limits
Age   
6574 years1.00  
7584 years0.830.820.84
85+ years0.590.580.61
Sex   
Female1.00  
Male0.850.830.86
Race   
White1.00  
Black0.780.760.80
Hispanic0.840.810.87
American Indian0.970.881.07
Asian/Pacific Islander0.740.700.79
Other0.940.920.97
Marital Status   
Married/partner1.00  
Single/separated/divorced0.960.940.98
Widowed0.960.950.98
Other0.930.900.95
Primary diagnosis   
Pneumonia1.00  
COPD0.830.810.85
Heart failure1.141.121.16
Ischemic stroke0.840.820.86
Acute MI1.951.902.01
Urinary tract infection1.061.031.09
Chest pain0.870.840.89
Comorbidities (yes or no)   
Hypertension0.980.970.99
Diabetes0.980.971.00
Chronic lung disease1.111.101.13
Fluid and electrolyte disorders1.261.241.27
Anemia deficiencies1.171.151.18
Congestive heart failure1.341.321.37
Hypothyroidism1.131.111.15
Peripheral vascular disease1.091.061.11
Depression1.381.351.41
Neurological disorders0.890.870.91
Renal failure1.231.201.26
Obesity1.111.081.14
Payer type   
Managed care1.00  
Not managed care1.041.021.06
Attending physician specialty   
Internal medicine1.00  
Cardiology1.321.281.36
Family/general medicine0.990.971.01
Geriatrics0.690.610.78
Hospitalist0.900.840.96
Nephrology1.020.961.08
Neurology0.930.861.00
Pulmonology1.101.051.15
Setting   
Rural1.00  
Urban1.021.001.05
 
Teaching status   
Nonteaching1.00  
Teaching1.010.981.03
Number of beds   
222001.00  
2004001.081.051.11
400+1.121.091.16
Region   
Midwest1.00  
Northeast0.850.830.88
South1.631.591.67
West1.431.381.47
Geriatrician presence   
No1.00  
Yes0.930.900.95

Physician specialty was also important. Adjusting for diagnosis attenuated some of this association, but compared with internists, cardiologists (OR 1.32, 95% CI 1.281.36) and pulmonologists (OR 1.10, 95% CI 1.051.15) were still more likely, hospitalists (OR 0.90, 95% CI 0.840.96) were less likely, and geriatricians (0.69, 95% CI 0.610.78) were least likely to prescribe any HS‐PIM.

Patient factors were also associated with HS‐PIM use. Compared with patients age 6574 years, patients older than 85 years were much less likely to receive an HS‐PIM (OR 0.59, CI 0.580.61), as to a lesser extent were nonwhites compared with whites and unmarried people compared with those who were married. Compared with patients with pneumonia, those with COPD, stroke, or chest pain were less likely and those with myocardial infarction and congestive heart failure were more likely to receive HS‐PIMs. Patients with a secondary diagnosis of depression were also at high risk (OR 1.38, CI 1.351.41).

DISCUSSION

Although Americans age 65 years and older make up less than 15% of the U.S. population, they consume about one third of all prescription drugs20 and account for one third of all hospital admissions.21 Using the Beers list, numerous studies have documented high rates of potentially inappropriate prescribing for community‐dwelling elderly and nursing home patients and, in some studies, an attendant risk of falling,2224 hip fracture,25, 26 hospitalization,13 or death.14 Applying these same criteria to a large sample of medical inpatients, we found that almost half received a potentially inappropriate drug, most of high severity. Moreover, the PIM prescribing rate varied substantially by region, hospital, and attending physician specialty. Although the use of PIMs was associated with patient age, comorbidities, and primary diagnosis, these patient factors explained only a small portion of the variation in prescribing practices across groups of physicians and hospitals.

Using consensus criteria, Beers originally found that 40% of the residents in 12 nursing homes received at least 1 PIM,8 and studies of community‐dwelling elderly demonstrated rates of 21% to 37%, with little change over time.6, 27, 28 Several small studies have examined inpatient prescribing.16, 17, 29, 30 The largest17 found that only 15% of elderly Italian inpatients received a PIM. Our finding, that 49% of inpatients had received at least 1 PIM, may partially reflect the high prevalence of use among elderly US patients in nursing homes and the community.

Regional variation has been demonstrated for ambulatory patients in the US6 and Europe.31 Zhan et al. found slightly higher rates of PIM use in the Midwest and the South (23%) than in the Northeast and the West (19%). Variation in Europe was greater, with 41% of patients in the Czech Republic versus 5.8% of patients in Denmark receiving at least 1 PIM. We found that region was the strongest predictor of in‐hospital HS‐PIM use, with patients in the South most likely and patients in the Northeast least likely to receive HS‐PIMs. This variation persisted even after adjusting for differences in other patient and hospital factors, suggesting that local custom played a large role in the decision to prescribe HS‐PIMs. Moreover, because outpatient rates are more uniform, these large differences seem limited to inpatient practice.

Patient factors have also been examined. Advanced age was associated with decreased PIM use in some studies17, 28, 31 but not in others.6, 27 We found increasing age to be strongly associated with decreased PIM use, suggesting that in the hospital, at least, doctors take care to avoid prescribing certain drugs to the frail elderly. Women appear to be consistently at higher risk than men,6, 27, 28, 31 and white patients are more at risk than those of other races.6 Our finding that certain diagnoses were associated with higher or lower rates has not been reported previously. The lower rates associated with stroke and COPD suggest that prescribers were aware that these patients were at increased risk of delirium and respiratory depression. The higher rates associated with myocardial infarction may have to do with the use of standardized order sets (eg, cath lab orders) that do not consider the age of the patient going for the procedure.

Admission to a geriatric service32 and intervention by a clinical pharmacist33 have been shown to decrease PIM prescribing at discharge. We noted that patients cared for by a geriatrician had the lowest rates of PIM prescribing during hospitalization as well and that hospitals with geriatricians had lower rates overall, possibly demonstrating that geriatricians had a ripple effect on their colleagues. Hospitalists also had lower rates than internists, supporting the notion that hospitalists provide higher‐quality inpatient care.

Our study had some important limitations. First, we only had access to inpatient administrative records. Thus, we could not identify which medications were continued from home and which were begun in the hospital, nor could we know the indications for which specific drugs were prescribed or who prescribed them. Based on published outpatient rates, however, we could assume that many of the drugs were started in the hospital and that others could have been discontinued but were not. Second, the Beers list was developed by the modified Delphi method; there was little empirical evidence of the danger of specific drugs, although some classes, such as benzodiazepines, opiates and digoxin, have been associated with inpatient falls.18, 3436 Furthermore, our administrative database did not allow us to balance the risks and benefits for particular patients; hence, the medications were only potentially inappropriate, and our study did not address the consequences of such prescribing. Although some of these drugs may be appropriate under certain circumstances, it is unlikely that these circumstances would vary by 60% across geographic regions or that internists would encounter these circumstances more often than do hospitalists. Thus, although we could not identify specific patients who received inappropriate medications, we did identify certain hospitals and even whole regions of the country in which the rate of inappropriate prescribing was high. Third, the Beers list, which was developed for outpatient use, may be less relevant in the inpatient setting. However, given that inpatients have more organ dysfunction and are at higher risk of delirium and falls, it may actually be more applicable to hospitalized patients. We similarly did not distinguish between single and multiple doses because the Beers list does not make such a distinction, and there is no empirical evidence that a single dose is safe. Indeed, patients are often at highest risk of falls immediately after initiation of therapy.3739 We did, however, exclude drugs such as laxatives, which may be appropriate for brief inpatient use but not for chronic use.

Our study also had a number of strengths. The large sample size, representing approximately 5% of annual inpatient admissions in the US over 2 years, offered an instructive look at the recent prescribing patterns of thousands of US physicians. We were able to identify many patient, physician, and hospital factors associated with PIM prescribing that have not previously been reported. Some of these factors, such as advanced age and comorbid diagnoses, suggest that physicians do tailor their treatment to individual patients. Nevertheless, patient factors accounted for only a small portion of the variation in prescribing. The largest variation, associated with regional, hospital, and physician factors, highlights the opportunity for improvement.

At the same time, our findings are encouraging for 2 reasons. First, most inappropriate prescribing involved only a handful of medications, so small changes in prescribing patterns could have a tremendous impact. Second, observing the practice of individual physicians and hospitals reveals what is possible. We found that in most specialties there were physicians who rarely or never used PIMs. We also found 7 hospitals, each with at least 300 cases, where no PIMs were ever prescribed.

Where should hospitals focus their efforts to prevent inappropriate prescribing? Our data highlight the complexity of the problem, which seems daunting. PIM prescribing is spread across all specialties, including geriatrics, and although cardiologists had the highest rate of prescribing, internists, who were more numerous, accounted for a much higher overall number of potentially inappropriate prescriptions. It would be instructive to study the 7 hospitals where PIMs were never prescribed or to interview those physicians who never prescribed PIMs, but the anonymous nature of our data would not allow for this. However, our data do suggest some directions. First, hospitals should become aware of their own rates of PIM use because measurement is the first step in quality improvement. Next, hospitals should focus efforts on reducing the use of the most common drugs. Eliminating just 3 drugs promethazine, diphenhydramine, and propoxyphenewould reduce the use of PIMs in 24% of elderly patients. Enlisting hospital pharmacists and electronic health records and reviewing standard order sets for elderly patients are potentially effective strategies. Finally, increasing the presence of geriatricians and hospitalists would be expected to have a modest impact.

In a representative sample of elderly inpatients, we found that almost half received a potentially inappropriate medication and that the rate of inappropriate prescribing varied widely among doctors and hospitals. Additional research is needed to distinguish which of the Beers drugs are most harmful and which patients are at highest risk. Research should also focus on understanding differences in prescribing patterns, perhaps by studying the outliers at both ends of the quality spectrum, and on techniques to minimize non‐patient‐centered variation.

Medications can be considered inappropriate when their risk outweighs their benefit. The Beers list1 identifies medications that should be avoided in persons 65 years or older because they are ineffective or pose an unnecessarily high risk or because a safer alternative is available. Initially developed in 1991, the list has gained wide acceptance and has been updated twice.2, 3 In July 1999 it was adopted by the Centers for Medicare & Medicaid Services (CMS) for nursing home regulation, and in 2006 the National Committee on Quality Assurance adopted a modified version as a Health Plan Employer Data and Information Set (HEDIS) measure of quality of care for older Americans.4

A number of studies have demonstrated that inappropriate prescribing is common in the ambulatory setting,57 in nursing homes,8, 9 and in emergency departments10, 11 and that exposure to inappropriate medications is associated with increased risk of adverse drug reactions12 and hospitalization.13, 14 Initial studies of hospitalized patients1517 suggest that potentially inappropriate prescribing is also common among elderly inpatients and that reducing the misuse of psychotropic medications can prevent falls.18 We report on the incidence of and risk factors associated with potentially inappropriate prescribing in a large sample of hospitalized elders.

METHODS

Patients

We conducted a retrospective cohort study using data from 384 hospitals participating in Perspective (Premier, Inc., Charlotte, NC), a database developed for measuring quality and health care utilization. Participating hospitals represent all regions of the United States and are primarily small‐ to medium‐sized nonteaching hospitals most of which are in urban areas. Premier collects data elements from participating hospitals via a custom data extract from hospitals' decision support system. Hospitals aggregate the data elements into their decision support systems from multiple information technology systems including billing, medical records, pharmacy, and laboratory systems. In addition to the information contained in the standard hospital discharge file, Perspective includes a date‐stamped log of all billed items, including medications with dose and quantity, for individual patients.

We included patients at least 65 years old admitted between September 1, 2002, and June 30, 2005, with a principal diagnosis of acute myocardial infarction, chronic obstructive pulmonary disease, chest pain, community‐acquired pneumonia, congestive heart failure, ischemic stroke, or urinary tract infection. International Classification of Diseases, Ninth Revision (ICD‐9‐CM) codes were used to identify diagnoses. Patients cared for by an attending physician with a surgical specialty were excluded. The study protocol was approved by the institutional review board of Baystate Medical Center.

Data Elements

For each patient, Perspective contains fields for age, sex, race, marital status, insurance status, principal diagnosis, comorbidities, and specialty of the attending physician. Comorbidities were identified from ICD‐9‐CM secondary diagnosis codes and APR‐DRGs using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser.19 Because almost all patients had Medicare coverage, plans were classified according to managed care status. Finally, for each patient we identified all medications administered, as well as discharge status, readmission rate, total costs, and length of stay. Hospitals were categorized by region (Northeast, South, Midwest, or West), bed size, setting (urban or rural), teaching status, and whether there were geriatricians.

Potentially Inappropriate Prescribing

Using the 2002 updated Beers criteria3 for potentially inappropriate medication (PIM) use in older adults, we identified the total number of PIMs administered to each patient during his or her hospital stay. We classified each PIM as either high or low severity based on the expert consensus expressed in the 1997 update of the Beers criteria.2 The list contains 48 PIMs and an additional 20 that should be avoided in patients with certain conditions. We did not include the second category of PIMs because we did not necessarily have sufficient patient information to make this determination. In addition, some of the standard PIMs, such as laxatives, although inappropriate for chronic outpatient use, could be appropriate in the hospital setting and were excluded from this analysis. Finally, several medications were considered inappropriate only above a given threshold (eg, lorazepam >3.0 mg/day) or for patients without a specific diagnosis (eg, digoxin >0.125 mg/day for patients without atrial fibrillation). We grouped PIMs that had similar side effects into 4 categories: sedatives, anticholinergics, causing orthostasis, or causing bleeding (Fig. 1).

Figure 1
Beers list of potentially inappropriate medications modified for hospitalized patients >65 years old.

Statistical Analysis

Summary statistics at the patient, physician, and hospital levels were constructed using frequencies and proportions for categorical data and means, standard deviations, medians, interquartile ranges, and box plots for continuous‐scale variables. Patients were identified as receiving a PIM if the drug was administered (above threshold dose where applicable) on at least 1 hospital day. We examined the association of each patient characteristic with use of any PIM, any high‐severity‐rated PIM, and each side effect category using chi‐square statistics. Kruskal‐Wallis analysis of variance was used to examine variation in hospital use rates by each hospital characteristic, and physician use rates for high‐severity PIMs by attending specialty. To examine whether it was feasible to avoid PIMs altogether, we compared individual hospitals as well as individual prescribers within their specialty, limiting the comparison to hospitals that contributed at least 100 patients and to physicians with at least 50 patients.

We developed a multivariable model for any high‐severity medication (HS‐PIM) use that included all patient, physician, and hospital characteristics except length of stay, mortality, cost, discharge status, and readmission rate. A generalized estimating equation model (SAS PROC GENMOD) with a logit link and a subcluster correlation structure was used to account for clustering at the hospital, physician, and diagnosis levels, adjusting for the clustering of primary diagnosis within physician level, nested within hospital level. Effects with P < .10 were retained in the model, and interaction effects were also evaluated for significance. Model fit was assessed using deviance and Pearson chi‐square statistics. All analyses were performed with SAS statistical software, version 9.1 (SAS Institute, Cary, NC).

RESULTS

We identified 519,853 patients at least 65 years old during the study period; 564 were excluded because of missing data for key variables or unclear principal diagnosis. An additional 25,318 were excluded because they were cared for by an attending with a surgical specialty. A total of 493,971 patients were included in the study (Table 1). Mean age was 78 years, and 24% of patients were 85 years or older. Forty‐three percent were male, 71% were white, and 39% were currently married. The most common principal diagnoses were community‐acquired pneumonia, congestive heart failure, and acute myocardial infarction. The most common comorbidities were hypertension, diabetes, and chronic pulmonary disease. Medicare was the primary payer for 91% of subjects, and 13% were in managed care plans. Most patients were cared for by internists (49%), family practitioners (18%), or cardiologists (17%). Only 1% of patients had a geriatrician as attending.

Characteristics of 493,971 Older Patients Hospitalized with 1 of 7 Common Medical Conditions
Characteristicn (%)
  • Other physician category includes 42 specialties.

Age group 
6574 years168,527 (34%)
7584 years206,407 (42%)
85+ years119,037 (24%)
Sex 
Male212,358 (43%)
Female281,613 (57%)
Race 
White351,331 (71%)
Black52,429 (11%)
Hispanic18,057 (4%)
American Indian1876 (0%)
Asian/Pacific Islander5926 (1%)
Other64,352 (13%)
Marital status 
Married/partner194,496 (39%)
Widowed155,273 (31%)
Single/separated/divorced75,964 (15%)
Other68,238 (14%)
Primary diagnosis 
Pneumonia122,732 (25%)
Heart failure109,071 (22%)
Acute MI70,581 (14%)
Ischemic stroke57,204 (12%)
Chest pain50,404 (10%)
COPD44,582 (9%)
Urinary tract infection39,397 (8%)
Comorbidities 
Hypertension310,163 (63%)
Diabetes151,755 (31%)
Chronic pulmonary disease134,900 (27%)
Fluid and electrolyte disorders128,703 (26%)
Deficiency anemias92,668 (19%)
Congestive heart failure69,201 (14%)
Hypothyroidism68,711 (14%)
Peripheral vascular disease47,244 (10%)
Depression41,507 (8%)
Other neurological disorders40,200 (8%)
Renal failure38,134 (8%)
Obesity25,143 (5%)
Payer type 
Not Managed care431,583 (87%)
Managed care62,388 (13%)
Attending physician specialty 
Internal medicine (internist)241,982 (49%)
Family/general medicine90,827 (18%)
Cardiology83,317 (17%)
Pulmonology21,163 (4%)
Hospitalist14,924 (3%)
Nephrology8257 (2%)
Neurology5800 (1%)
Geriatrics3099 (1%)
Other*24,602 (5%)
 
Mortality 
Expired28,321 (6%)
Alive465,650 (94%)
Discharge status, n (% of survivors) 
Home323,629 (66%)
Nursing care119,468 (24%)
Transfer/short‐term hospital13,531 (3%)
Hospice9022 (2%)
14‐Day readmission, n (% of survivors) 
Yes35,309 (8%)
No430,334 (92%)
Length of stay (days), median (IQR)4 (2, 7)
Total cost (dollars)$5513 ($3366, $9902)

Just under half of all patients (49%) received at least 1 PIM, and 6% received 3 or more (Table 2). Thirty‐eight percent received at least 1 drug with a high severity rating (HS‐PIM). The most common PIMs were promethazine, diphenhydramine, propoxyphene, clonidine, amiodarone, and lorazepam (>3 mg/day).

Number and Type of Potentially Inappropriate Medications (PIMs) Prescribed
 Patients, n (%)
Number of PIMs 
0254,200 (51%)
1146,028 (30%)
261,445 (12%)
322,128 (4%)
4+10,170 (2%)
Number of high‐severity‐rated PIMs 
0304,523 (62%)
1129,588 (26%)
243,739 (9%)
312,213 (2%)
4+3908 (1%)
Use of any PIM by side effect class 
Sedatives156,384 (32%)
Anticholinergic effects109,293 (22%)
Causing orthostasis43,805 (9%)
Causing bleeding14,744 (3%)
Most commonly prescribed 
Promethazine49,888 (10%)
Diphenhydramine45,458 (9%)
Propoxyphene41,786 (8%)
Clonidine34,765 (7%)
Amiodarone34,318 (7%)
Lorazepam (>3 mg/day)25,147 (5%)

Patient, Physician, and Hospital Factors Associated with PIMs

Patient, physician, and hospital characteristics were all associated with use of PIMs (Table 3). In univariate analyses, older patients were less likely to receive any class of PIM, and this difference was accentuated for HS‐PIMs. Women, American Indians, married people, and those not in managed care plans were slightly more likely to receive PIMs, whereas patients admitted with acute myocardial infarction or congestive heart failure were even more likely to receive PIMs (P < .0001 for all comparisons).

Potentially Inappropriate Medication Use by Patient Characteristic
Patient characteristicAny PIM n (row %)Any high‐severity PIM n (row %)Sedatives n (row %)Anticholinergic effects n (row %)Causing orthostasis n (row %)Causing bleeding n (row %)
  • An additional 24,602 patients (5%) seen by 42 additional physician specialties were excluded from this analysis.

  • Chi‐square tests indicated all differences by patient characteristics were significant at P < .0001 except there was no significant difference by managed care status for use rates of drugs potentially causing orthostasis or drugs potentially causing bleeding.

Overall239,771 (49%)189,448 (38%)156,384 (32%)109,293 (22%)43,805 (9%)14,744 (3%)
Age group
6574 years89,168 (53%)72,573 (43%)61,399 (36%)44,792 (27%)15,799 (9%)6655 (4%)
7584 years100,787 (49%)79,595 (39%)65,034 (32%)45,121 (22%)18,519 (9%)5727 (3%)
85+ years49,816 (42%)37,280 (31%)29,951 (25%)19,380 (16%)9487 (8%)2362 (2%)
Sex
Male100,824 (47%)79,535 (37%)63,591 (30%)42,754 (20%)17,885 (8%)5771 (3%)
Female138,947 (49%)109,913 (39%)92,793 (33%)66,539 (24%)25,920 (9%)8973 (3%)
Race
White173,481 (49%)139,941 (40%)112,556 (32%)81,097 (23%)27,555 (8%)10,590 (3%)
Black26,793 (51%)18,655 (36%)18,720 (36%)11,263 (21%)8925 (17%)1536 (3%)
Hispanic8509 (47%)6370 (35%)5549 (31%)3505 (19%)2047 (11%)648 (4%)
American Indian1091 (58%)849 (45%)818 (44%)563 (30%)190 (10%)76 (4%)
Asian/Pacific Islander2386 (40%)1896 (32%)1420 (24%)1023 (17%)519 (9%)127 (2%)
Other27,511 (43%)21,737 (34%)17,321 (27%)11,842 (18%)4569 (7%)1767 (3%)
Marital status
Married/partner96,874 (50%)77,803 (40%)63,303 (33%)45,042 (23%)16,765 (9%)5969 (3%)
Widowed74,622 (48%)58,012 (37%)48,367 (31%)33,516 (22%)13,865 (9%)4354 (3%)
Single/separated/divorced36,583 (48%)28,799 (38%)24,251 (32%)16,115 (21%)7229 (10%)2399 (3%)
Other31,692 (46%)24,834 (36%)20,463 (30%)14,620 (21%)5946 (9%)2022 (3%)
Primary diagnosis
Pneumonia56,845 (46%)46,271 (38%)35,353 (29%)25,484 (21%)9184 (7%)4155 (3%)
Heart failure56,460 (52%)42,231 (39%)34,340 (31%)22,093 (20%)10,117 (9%)1945 (2%)
Acute MI43,046 (61%)37,849 (54%)32,560 (46%)25,568 (36%)4738 (7%)2549 (4%)
Ischemic stroke25,763 (45%)17,613 (31%)18,500 (32%)8742 (15%)9644 (17%)1384 (2%)
Chest pain20,655 (41%)16,363 (32%)13,536 (27%)10,520 (21%)3474 (7%)2027 (4%)
COPD18,876 (42%)14,626 (33%)12,087 (27%)8096 (18%)3209 (7%)1139 (3%)
Urinary tract infection18,126 (46%)14,495 (37%)10,008 (25%)8790 (22%)3439 (9%)1545 (4%)
Payer type
Nonmanaged care212,150 (49%)168,013 (39%)138,679 (32%)97,776 (23%)38,341 (9%)12,868 (3%)
Managed care27,621 (44%)21,435 (34%)17,705 (28%)11,517 (18%)5464 (9%)1876 (3%)
Attending physician specialty*
Internal medicine (internist%)112,664 (47%)86,907 (36%)71,382 (30%)48,746 (20%)23,221 (10%)7086 (3%)
Family/general medicine41,303 (45%)32,338 (36%)25,653 (28%)18,274 (20%)7660 (8%)2852 (3%)
Cardiology48,485 (58%)40,752 (49%)34,859 (42%)25,792 (31%)5455 (7%)2542 (3%)
Pulmonology10,231 (48%)8105 (38%)6746 (32%)4064 (19%)1739 (8%)574 (3%)
Hospitalist7003 (47%)5443 (36%)4447 (30%)3179 (21%)1471 (10%)463 (3%)
Nephrology4508 (55%)3388 (41%)3132 (38%)2054 (25%)1326 (16%)198 (2%)
Neurology2420 (42%)1789 (31%)1625 (28%)851 (15%)699 (12%)174 (3%)
Geriatrics1020 (33%)785 (25%)596 (19%)404 (13%)196 (6%)41 (1%)

The HS‐PIM prescribing varied substantially by attending specialty (Fig. 2). Internists, family practitioners, and hospitalists all had similar median rates (33%), cardiologists had a higher median rate (48%), and geriatricians had a lower rate (24%). The most common PIM also differed by specialty: whereas promethazine was the most commonly prescribed drug across most specialties, nephrologists and neurologists used clonidine, pulmonologists used lorazepam, and cardiologists used diphenhydramine most often. Among the 8% of physicians who saw at least 50 patients, there was also great variation in each specialty (Fig. 2). Among internists and cardiologists who saw at least 50 patients, the high‐severity PIM usage rate ranged from 0% to more than 90%.

Figure 2
Distribution of physician prescribing rates for high‐severity‐rated PIM by specialty for physicians treating at least 50 hospitalized patients during the study period (N, number of physicians in each specialty). The line in the middle of each box represents the median use rate across physicians, and the box extends to the interquartile range (IQR). The lines emerging from the box extend to the adjacent values. The upper adjacent value is defined as the largest data point less than or equal to the 75th percentile plus 1.5 times the IQR; the lower adjacent value is defined as the smallest data point greater than or equal to the 25th percentile minus 1.5 IQR. Observed points more extreme than the adjacent values are individually plotted.

There was substantial variation in PIM usage among hospitals, most notably by region. The mean proportion of patients receiving PIMs ranged from 34% at hospitals in the Northeast to 55% at hospitals in the South (Table 4). Smaller hospitals and those in urban settings had slightly lower rates, as did those that had geriatricians on staff. The teaching status of the hospital had little effect. Variation at the individual hospital level was extreme (Fig. 3). Although half of all hospitals had rates between 43% and 58%, in 7 hospitals with more than 300 encounters each, PIMs were never prescribed for geriatric patients.

Figure 3
Distribution of hospital use rates for each category of PIM among hospitals with at least 100 eligible patients during the study period. Thirteen hospitals (3%) and 324 patients (<0.1%) were excluded. For interpretation of the box plots, see Figure 2.
Percentage of Patients Prescribed Potentially Inappropriate Medication (PIM) by Hospital Characteristic
 Hospitals Total = 384 n (%)Patients N = 49,3971 n (%)Any PIM Mean = 48.2 Mean (SD)Any high‐severity PIM Mean = 38.7 Mean (SD)Sedatives Mean = 30.2 Mean (SD)Anticholinergic effects Mean = 21.5 Mean (SD)Causing orthostasis Mean = 8.5 Mean (SD)Causing bleeding Mean = 3.1 Mean (SD)
  • Note: P values from analysis of variance of hospital use rates for each hospital characteristic.

  • P < .05,

  • P < .001,

  • P < .0001.

Hospital region  *****************
Midwest76 (20%)95,791 (19%)38.8 (19.7)30.0 (16.4)24.3 (13.8)15.1 (9.9)6.9 (6.3)3.1 (2.3)
Northeast47 (12%)79,138 (16%)34.1 (12.6)26.2 (11.2)19.0 (9.2)13.5 (8.1)4.9 (2.3)2.1 (1.6)
South199 (52%)260,870 (53%)54.5 (10.1)42.7 (9.6)36.0 (10.8)26.4 (8.6)10.4 (4.6)3.6 (2.5)
West62 (16%)58,172 (12%)45.8 (8.1)37.4 (7.1)27.3 (7.7)19.5 (5.7)7.4 (4.8)2.7 (1.3)
Teaching status        
Nonteaching297 (77%)324,948 (66%)47.3 (14.6)36.9 (12.3)29.8 (12.0)21.3 (9.9)8.7 (5.4)3.3 (2.4)
Teaching87 (23%)169,023 (34%)48.2 (16.0)38.8 (14.2)31.6 (14.5)22.1 (10.2)7.8 (4.4)2.7 (1.5)
Staffed beds   ***   
22200143 (37%)80,741 (16%)45.5 (16.9)35.2 (14.6)27.5 (14.0)20.1 (10.3)8.0 (6.2)3.5 (3.1)
200400137 (36%)177,286 (36%)47.7 (14.2)37.8 (12.0)30.5 (11.6)22.0 (10.0)8.4 (4.7)3.0 (1.6)
400+104 (27%)235944 (48%)50.1 (12.4)39.6 (10.6)33.5 (10.9)22.7 (9.3)9.3 (4.2)2.9 (1.4)
Population serviced     ****
Rural119 (31%)102,799 (21%)48.4 (13.0)38.3 (10.6)29.2 (11.0)23.2 (9.3)7.5 (4.0)3.7 (3.0)
Urban265 (69%)391,172 (79%)47.1 (15.7)36.9 (13.7)30.6 (13.2)20.7 (10.2)9.0 (5.6)2.9 (1.8)
Geriatrician presence        
No340 (89%)409,281 (83%)47.7 (15.3)37.6 (13.0)30.3 (12.8)21.7 (10.0)8.4 (5.3)3.2 (2.3)
Yes44 (11%)84,690 (17%)45.8 (11.4)35.5 (10.6)29.4 (10.8)19.6 (9.4)9.3 (4.3)2.9 (1.6)

Multivariable Model

In a multivariable logit model that included all patient, hospital, and physician characteristics and that accounted for clustering at the hospital, physician, and diagnosis levels, several characteristics were associated with HS‐PIM prescribing (Table 5). By far the most important predictor of use was hospital region. Compared with patients at hospitals in the Midwest, patients in the South (OR 1.63, 95% CI 1.591.67) and West (OR 1.43, 95% CI 1.381.47) were more likely and those in the Northeast were less likely (OR 0.85, 95% CI 0.830.88) to receive HS‐PIMs. Larger hospitals had higher HS‐PIM rates than smaller ones, but teaching status and rural or urban setting were not associated with HS‐PIM prescribing. The presence of geriatricians in a hospital was also associated with lower HS‐PIM prescribing for the entire hospital.

Adjusted Odds Ratio Estimates for High‐Severity PIM Use
Effect (reference)Odds ratio95% Confidence limits
Age   
6574 years1.00  
7584 years0.830.820.84
85+ years0.590.580.61
Sex   
Female1.00  
Male0.850.830.86
Race   
White1.00  
Black0.780.760.80
Hispanic0.840.810.87
American Indian0.970.881.07
Asian/Pacific Islander0.740.700.79
Other0.940.920.97
Marital Status   
Married/partner1.00  
Single/separated/divorced0.960.940.98
Widowed0.960.950.98
Other0.930.900.95
Primary diagnosis   
Pneumonia1.00  
COPD0.830.810.85
Heart failure1.141.121.16
Ischemic stroke0.840.820.86
Acute MI1.951.902.01
Urinary tract infection1.061.031.09
Chest pain0.870.840.89
Comorbidities (yes or no)   
Hypertension0.980.970.99
Diabetes0.980.971.00
Chronic lung disease1.111.101.13
Fluid and electrolyte disorders1.261.241.27
Anemia deficiencies1.171.151.18
Congestive heart failure1.341.321.37
Hypothyroidism1.131.111.15
Peripheral vascular disease1.091.061.11
Depression1.381.351.41
Neurological disorders0.890.870.91
Renal failure1.231.201.26
Obesity1.111.081.14
Payer type   
Managed care1.00  
Not managed care1.041.021.06
Attending physician specialty   
Internal medicine1.00  
Cardiology1.321.281.36
Family/general medicine0.990.971.01
Geriatrics0.690.610.78
Hospitalist0.900.840.96
Nephrology1.020.961.08
Neurology0.930.861.00
Pulmonology1.101.051.15
Setting   
Rural1.00  
Urban1.021.001.05
 
Teaching status   
Nonteaching1.00  
Teaching1.010.981.03
Number of beds   
222001.00  
2004001.081.051.11
400+1.121.091.16
Region   
Midwest1.00  
Northeast0.850.830.88
South1.631.591.67
West1.431.381.47
Geriatrician presence   
No1.00  
Yes0.930.900.95

Physician specialty was also important. Adjusting for diagnosis attenuated some of this association, but compared with internists, cardiologists (OR 1.32, 95% CI 1.281.36) and pulmonologists (OR 1.10, 95% CI 1.051.15) were still more likely, hospitalists (OR 0.90, 95% CI 0.840.96) were less likely, and geriatricians (0.69, 95% CI 0.610.78) were least likely to prescribe any HS‐PIM.

Patient factors were also associated with HS‐PIM use. Compared with patients age 6574 years, patients older than 85 years were much less likely to receive an HS‐PIM (OR 0.59, CI 0.580.61), as to a lesser extent were nonwhites compared with whites and unmarried people compared with those who were married. Compared with patients with pneumonia, those with COPD, stroke, or chest pain were less likely and those with myocardial infarction and congestive heart failure were more likely to receive HS‐PIMs. Patients with a secondary diagnosis of depression were also at high risk (OR 1.38, CI 1.351.41).

DISCUSSION

Although Americans age 65 years and older make up less than 15% of the U.S. population, they consume about one third of all prescription drugs20 and account for one third of all hospital admissions.21 Using the Beers list, numerous studies have documented high rates of potentially inappropriate prescribing for community‐dwelling elderly and nursing home patients and, in some studies, an attendant risk of falling,2224 hip fracture,25, 26 hospitalization,13 or death.14 Applying these same criteria to a large sample of medical inpatients, we found that almost half received a potentially inappropriate drug, most of high severity. Moreover, the PIM prescribing rate varied substantially by region, hospital, and attending physician specialty. Although the use of PIMs was associated with patient age, comorbidities, and primary diagnosis, these patient factors explained only a small portion of the variation in prescribing practices across groups of physicians and hospitals.

Using consensus criteria, Beers originally found that 40% of the residents in 12 nursing homes received at least 1 PIM,8 and studies of community‐dwelling elderly demonstrated rates of 21% to 37%, with little change over time.6, 27, 28 Several small studies have examined inpatient prescribing.16, 17, 29, 30 The largest17 found that only 15% of elderly Italian inpatients received a PIM. Our finding, that 49% of inpatients had received at least 1 PIM, may partially reflect the high prevalence of use among elderly US patients in nursing homes and the community.

Regional variation has been demonstrated for ambulatory patients in the US6 and Europe.31 Zhan et al. found slightly higher rates of PIM use in the Midwest and the South (23%) than in the Northeast and the West (19%). Variation in Europe was greater, with 41% of patients in the Czech Republic versus 5.8% of patients in Denmark receiving at least 1 PIM. We found that region was the strongest predictor of in‐hospital HS‐PIM use, with patients in the South most likely and patients in the Northeast least likely to receive HS‐PIMs. This variation persisted even after adjusting for differences in other patient and hospital factors, suggesting that local custom played a large role in the decision to prescribe HS‐PIMs. Moreover, because outpatient rates are more uniform, these large differences seem limited to inpatient practice.

Patient factors have also been examined. Advanced age was associated with decreased PIM use in some studies17, 28, 31 but not in others.6, 27 We found increasing age to be strongly associated with decreased PIM use, suggesting that in the hospital, at least, doctors take care to avoid prescribing certain drugs to the frail elderly. Women appear to be consistently at higher risk than men,6, 27, 28, 31 and white patients are more at risk than those of other races.6 Our finding that certain diagnoses were associated with higher or lower rates has not been reported previously. The lower rates associated with stroke and COPD suggest that prescribers were aware that these patients were at increased risk of delirium and respiratory depression. The higher rates associated with myocardial infarction may have to do with the use of standardized order sets (eg, cath lab orders) that do not consider the age of the patient going for the procedure.

Admission to a geriatric service32 and intervention by a clinical pharmacist33 have been shown to decrease PIM prescribing at discharge. We noted that patients cared for by a geriatrician had the lowest rates of PIM prescribing during hospitalization as well and that hospitals with geriatricians had lower rates overall, possibly demonstrating that geriatricians had a ripple effect on their colleagues. Hospitalists also had lower rates than internists, supporting the notion that hospitalists provide higher‐quality inpatient care.

Our study had some important limitations. First, we only had access to inpatient administrative records. Thus, we could not identify which medications were continued from home and which were begun in the hospital, nor could we know the indications for which specific drugs were prescribed or who prescribed them. Based on published outpatient rates, however, we could assume that many of the drugs were started in the hospital and that others could have been discontinued but were not. Second, the Beers list was developed by the modified Delphi method; there was little empirical evidence of the danger of specific drugs, although some classes, such as benzodiazepines, opiates and digoxin, have been associated with inpatient falls.18, 3436 Furthermore, our administrative database did not allow us to balance the risks and benefits for particular patients; hence, the medications were only potentially inappropriate, and our study did not address the consequences of such prescribing. Although some of these drugs may be appropriate under certain circumstances, it is unlikely that these circumstances would vary by 60% across geographic regions or that internists would encounter these circumstances more often than do hospitalists. Thus, although we could not identify specific patients who received inappropriate medications, we did identify certain hospitals and even whole regions of the country in which the rate of inappropriate prescribing was high. Third, the Beers list, which was developed for outpatient use, may be less relevant in the inpatient setting. However, given that inpatients have more organ dysfunction and are at higher risk of delirium and falls, it may actually be more applicable to hospitalized patients. We similarly did not distinguish between single and multiple doses because the Beers list does not make such a distinction, and there is no empirical evidence that a single dose is safe. Indeed, patients are often at highest risk of falls immediately after initiation of therapy.3739 We did, however, exclude drugs such as laxatives, which may be appropriate for brief inpatient use but not for chronic use.

Our study also had a number of strengths. The large sample size, representing approximately 5% of annual inpatient admissions in the US over 2 years, offered an instructive look at the recent prescribing patterns of thousands of US physicians. We were able to identify many patient, physician, and hospital factors associated with PIM prescribing that have not previously been reported. Some of these factors, such as advanced age and comorbid diagnoses, suggest that physicians do tailor their treatment to individual patients. Nevertheless, patient factors accounted for only a small portion of the variation in prescribing. The largest variation, associated with regional, hospital, and physician factors, highlights the opportunity for improvement.

At the same time, our findings are encouraging for 2 reasons. First, most inappropriate prescribing involved only a handful of medications, so small changes in prescribing patterns could have a tremendous impact. Second, observing the practice of individual physicians and hospitals reveals what is possible. We found that in most specialties there were physicians who rarely or never used PIMs. We also found 7 hospitals, each with at least 300 cases, where no PIMs were ever prescribed.

Where should hospitals focus their efforts to prevent inappropriate prescribing? Our data highlight the complexity of the problem, which seems daunting. PIM prescribing is spread across all specialties, including geriatrics, and although cardiologists had the highest rate of prescribing, internists, who were more numerous, accounted for a much higher overall number of potentially inappropriate prescriptions. It would be instructive to study the 7 hospitals where PIMs were never prescribed or to interview those physicians who never prescribed PIMs, but the anonymous nature of our data would not allow for this. However, our data do suggest some directions. First, hospitals should become aware of their own rates of PIM use because measurement is the first step in quality improvement. Next, hospitals should focus efforts on reducing the use of the most common drugs. Eliminating just 3 drugs promethazine, diphenhydramine, and propoxyphenewould reduce the use of PIMs in 24% of elderly patients. Enlisting hospital pharmacists and electronic health records and reviewing standard order sets for elderly patients are potentially effective strategies. Finally, increasing the presence of geriatricians and hospitalists would be expected to have a modest impact.

In a representative sample of elderly inpatients, we found that almost half received a potentially inappropriate medication and that the rate of inappropriate prescribing varied widely among doctors and hospitals. Additional research is needed to distinguish which of the Beers drugs are most harmful and which patients are at highest risk. Research should also focus on understanding differences in prescribing patterns, perhaps by studying the outliers at both ends of the quality spectrum, and on techniques to minimize non‐patient‐centered variation.

References
  1. Beers MH,Ouslander JG,Rollingher I,Reuben DB,Brooks J,Beck JC.Explicit criteria for determining inappropriate medication use in nursing home residents. UCLA Division of Geriatric Medicine.Arch Intern Med.1991;151:18251832.
  2. Beers MH.Explicit criteria for determining potentially inappropriate medication use by the elderly. An update.Arch Intern Med.1997;157:15311536.
  3. Fick DM,Cooper JW,Wade WE,Waller JL,Maclean JR,Beers MH.Updating the Beers criteria for potentially inappropriate medication use in older adults: results of a US consensus panel of experts.Arch Intern Med.2003;163:27162724.
  4. National Committee on Quality Assurance. Drugs to be Avoided in the Elderly. Available at: http://www.ncqa.org/Programs/HEDIS/2006/Volume2/NDC/DAE_06.xls. Accessed November 20,2006.
  5. Curtis LH,Ostbye T,Sendersky V, et al.Inappropriate prescribing for elderly Americans in a large outpatient population.Arch Intern Med.2004;164:16211625.
  6. Zhan C,Sangl J,Bierman AS, et al.Potentially inappropriate medication use in the community‐dwelling elderly: findings from the 1996 Medical Expenditure Panel Survey.JAMA.2001;286:28232829.
  7. Mort JR,Aparasu RR.Prescribing potentially inappropriate psychotropic medications to the ambulatory elderly.Arch Intern Med.2000;160:28252831.
  8. Beers MH,Ouslander JG,Fingold SF, et al.Inappropriate medication prescribing in skilled‐nursing facilities.Ann Intern Med.1992;117:684689.
  9. Perri M,Menon AM,Deshpande AD, et al.Adverse outcomes associated with inappropriate drug use in nursing homes.Ann Pharmacother.2005;39:405411.
  10. Caterino JM,Emond JA,Camargo CA.Inappropriate medication administration to the acutely ill elderly: a nationwide emergency department study, 1992–2000.J Am Geriatr Soc.2004;52:18471855.
  11. Chin MH,Wang LC,Jin L, et al.Appropriateness of medication selection for older persons in an urban academic emergency department.Acad Emerg Med.1999;6:12321242.
  12. Chang CM,Liu PY,Yang YH,Yang YC,Wu CF,Lu FH.Use of the Beers criteria to predict adverse drug reactions among first‐visit elderly outpatients.Pharmacotherapy.2005;25:831838.
  13. Klarin I,Wimo A,Fastbom J.The association of inappropriate drug use with hospitalisation and mortality: a population‐based study of the very old.Drugs Aging.2005;22(1):6982.
  14. Lau DT,Kasper JD,Potter DE,Lyles A,Bennett RG.Hospitalization and death associated with potentially inappropriate medication prescriptions among elderly nursing home residents.Arch Intern Med.2005;165(1):6874.
  15. Edwards RF,Harrison TM,Davis SM.Potentially inappropriate prescribing for geriatric inpatients: an acute care of the elderly unit compared to a general medicine service.Consult Pharm.2003;18(1):3742, 47–39.
  16. Hanlon JT,Artz MB,Pieper CF, et al.Inappropriate medication use among frail elderly inpatients.Ann Pharmacother.2004;38(1):914.
  17. Onder G,Landi F,Cesari M,Gambassi G,Carbonin P,Bernabei R.Inappropriate medication use among hospitalized older adults in Italy: results from the Italian Group of Pharmacoepidemiology in the Elderly.Eur J Clin Pharmacol.2003;59(2):157162.
  18. Peterson JF,Kuperman GJ,Shek C,Patel M,Avorn J,Bates DW.Guided prescription of psychotropic medications for geriatric inpatients.Arch Intern Med.2005;165:802807.
  19. Elixhauser A,Steiner C,Harris DR,Coffey RM.Comorbidity measures for use with administrative data.Med Care.1998;36(1):827.
  20. Soumerai SB,Ross‐Degnan D.Inadequate prescription‐drug coverage for Medicare enrollees—a call to action.N Engl J Med.1999;340:722728.
  21. National and regional estimates on hospital use for all patients from the HCUP Nationwide Inpatient Sample (NIS). Agency for Healthcare Research and Quality (AHRQ). Available at: http://hcupnet.ahrq.gov/HCUPnet.jsp. Accessed October 12,2006.
  22. French DD,Campbell R,Spehar A,Cunningham F,Bulat T,Luther SL.Drugs and falls in community‐dwelling older people: a national veterans study.Clin Ther.2006;28:619630.
  23. Landi F,Onder G,Cesari M,Barillaro C,Russo A,Bernabei R.Psychotropic medications and risk for falls among community‐dwelling frail older people: an observational study.J Gerontol A Biol Sci Med Sci.2005;60:622626.
  24. Leipzig RM,Cumming RG,Tinetti ME.Drugs and falls in older people: a systematic review and meta‐analysis: I. Psychotropic drugs.J Am Geriatr Soc.1999;47(1):3039.
  25. Kamal‐Bahl SJ,Stuart BC,Beers MH.Propoxyphene use and risk for hip fractures in older adults.Am J Geriatr Pharmacother.2006;4:219226.
  26. Ensrud KE,Blackwell T,Mangione CM, et al.Central nervous system active medications and risk for fractures in older women.Arch Intern Med.2003;163:949957.
  27. Simon SR,Chan KA,Soumerai SB, et al.Potentially inappropriate medication use by elderly persons in U.S. Health Maintenance Organizations, 2000–2001.J Am Geriatr Soc.2005;53:227232.
  28. Goulding MR.Inappropriate medication prescribing for elderly ambulatory care patients.Arch Intern Med.2004;164:305312.
  29. Passarelli MC,Jacob‐Filho W,Figueras A.Adverse drug reactions in an elderly hospitalised population: inappropriate prescription is a leading cause.Drugs Aging.2005;22:767777.
  30. Raivio MM,Laurila JV,Strandberg TE,Tilvis RS,Pitkala KH.Use of inappropriate medications and their prognostic significance among in‐hospital and nursing home patients with and without dementia in Finland.Drugs Aging.2006;23:333343.
  31. Fialova D,Topinkova E,Gambassi G, et al.Potentially inappropriate medication use among elderly home care patients in Europe.JAMA.2005;293:13481358.
  32. Laroche ML,Charmes JP,Nouaille Y,Fourrier A,Merle L.Impact of hospitalisation in an acute medical geriatric unit on potentially inappropriate medication use.Drugs Aging.2006;23(1):4959.
  33. Brown BK,Earnhart J.Pharmacists and their effectiveness in ensuring the appropriateness of the chronic medication regimens of geriatric inpatients.Consult Pharm.2004;19:432436.
  34. Passaro A,Volpato S,Romagnoni F,Manzoli N,Zuliani G,Fellin R.Benzodiazepines with different half‐life and falling in a hospitalized population: the GIFA study. Gruppo Italiano di Farmacovigilanza nell'Anziano.J Clin Epidemiol.2000;53:12221229.
  35. Gales BJ,Menard SM.Relationship between the administration of selected medications and falls in hospitalized elderly patients.Ann Pharmacother.1995;29:354358.
  36. Mendelson WB.The use of sedative/hypnotic medication and its correlation with falling down in the hospital.Sleep.1996;19:698701.
  37. Wagner AK,Zhang F,Soumerai SB, et al.Benzodiazepine use and hip fractures in the elderly: who is at greatest risk?Arch Intern Med.2004;164:15671572.
  38. Wang PS,Bohn RL,Glynn RJ,Mogun H,Avorn J.Hazardous benzodiazepine regimens in the elderly: effects of half‐life, dosage, and duration on risk of hip fracture.Am J Psychiatry.2001;158:892898.
  39. Tamblyn R,Abrahamowicz M,du Berger R,McLeod P,Bartlett G.A 5‐year prospective assessment of the risk associated with individual benzodiazepines and doses in new elderly users.J Am Geriatr Soc.2005;53:233241.
References
  1. Beers MH,Ouslander JG,Rollingher I,Reuben DB,Brooks J,Beck JC.Explicit criteria for determining inappropriate medication use in nursing home residents. UCLA Division of Geriatric Medicine.Arch Intern Med.1991;151:18251832.
  2. Beers MH.Explicit criteria for determining potentially inappropriate medication use by the elderly. An update.Arch Intern Med.1997;157:15311536.
  3. Fick DM,Cooper JW,Wade WE,Waller JL,Maclean JR,Beers MH.Updating the Beers criteria for potentially inappropriate medication use in older adults: results of a US consensus panel of experts.Arch Intern Med.2003;163:27162724.
  4. National Committee on Quality Assurance. Drugs to be Avoided in the Elderly. Available at: http://www.ncqa.org/Programs/HEDIS/2006/Volume2/NDC/DAE_06.xls. Accessed November 20,2006.
  5. Curtis LH,Ostbye T,Sendersky V, et al.Inappropriate prescribing for elderly Americans in a large outpatient population.Arch Intern Med.2004;164:16211625.
  6. Zhan C,Sangl J,Bierman AS, et al.Potentially inappropriate medication use in the community‐dwelling elderly: findings from the 1996 Medical Expenditure Panel Survey.JAMA.2001;286:28232829.
  7. Mort JR,Aparasu RR.Prescribing potentially inappropriate psychotropic medications to the ambulatory elderly.Arch Intern Med.2000;160:28252831.
  8. Beers MH,Ouslander JG,Fingold SF, et al.Inappropriate medication prescribing in skilled‐nursing facilities.Ann Intern Med.1992;117:684689.
  9. Perri M,Menon AM,Deshpande AD, et al.Adverse outcomes associated with inappropriate drug use in nursing homes.Ann Pharmacother.2005;39:405411.
  10. Caterino JM,Emond JA,Camargo CA.Inappropriate medication administration to the acutely ill elderly: a nationwide emergency department study, 1992–2000.J Am Geriatr Soc.2004;52:18471855.
  11. Chin MH,Wang LC,Jin L, et al.Appropriateness of medication selection for older persons in an urban academic emergency department.Acad Emerg Med.1999;6:12321242.
  12. Chang CM,Liu PY,Yang YH,Yang YC,Wu CF,Lu FH.Use of the Beers criteria to predict adverse drug reactions among first‐visit elderly outpatients.Pharmacotherapy.2005;25:831838.
  13. Klarin I,Wimo A,Fastbom J.The association of inappropriate drug use with hospitalisation and mortality: a population‐based study of the very old.Drugs Aging.2005;22(1):6982.
  14. Lau DT,Kasper JD,Potter DE,Lyles A,Bennett RG.Hospitalization and death associated with potentially inappropriate medication prescriptions among elderly nursing home residents.Arch Intern Med.2005;165(1):6874.
  15. Edwards RF,Harrison TM,Davis SM.Potentially inappropriate prescribing for geriatric inpatients: an acute care of the elderly unit compared to a general medicine service.Consult Pharm.2003;18(1):3742, 47–39.
  16. Hanlon JT,Artz MB,Pieper CF, et al.Inappropriate medication use among frail elderly inpatients.Ann Pharmacother.2004;38(1):914.
  17. Onder G,Landi F,Cesari M,Gambassi G,Carbonin P,Bernabei R.Inappropriate medication use among hospitalized older adults in Italy: results from the Italian Group of Pharmacoepidemiology in the Elderly.Eur J Clin Pharmacol.2003;59(2):157162.
  18. Peterson JF,Kuperman GJ,Shek C,Patel M,Avorn J,Bates DW.Guided prescription of psychotropic medications for geriatric inpatients.Arch Intern Med.2005;165:802807.
  19. Elixhauser A,Steiner C,Harris DR,Coffey RM.Comorbidity measures for use with administrative data.Med Care.1998;36(1):827.
  20. Soumerai SB,Ross‐Degnan D.Inadequate prescription‐drug coverage for Medicare enrollees—a call to action.N Engl J Med.1999;340:722728.
  21. National and regional estimates on hospital use for all patients from the HCUP Nationwide Inpatient Sample (NIS). Agency for Healthcare Research and Quality (AHRQ). Available at: http://hcupnet.ahrq.gov/HCUPnet.jsp. Accessed October 12,2006.
  22. French DD,Campbell R,Spehar A,Cunningham F,Bulat T,Luther SL.Drugs and falls in community‐dwelling older people: a national veterans study.Clin Ther.2006;28:619630.
  23. Landi F,Onder G,Cesari M,Barillaro C,Russo A,Bernabei R.Psychotropic medications and risk for falls among community‐dwelling frail older people: an observational study.J Gerontol A Biol Sci Med Sci.2005;60:622626.
  24. Leipzig RM,Cumming RG,Tinetti ME.Drugs and falls in older people: a systematic review and meta‐analysis: I. Psychotropic drugs.J Am Geriatr Soc.1999;47(1):3039.
  25. Kamal‐Bahl SJ,Stuart BC,Beers MH.Propoxyphene use and risk for hip fractures in older adults.Am J Geriatr Pharmacother.2006;4:219226.
  26. Ensrud KE,Blackwell T,Mangione CM, et al.Central nervous system active medications and risk for fractures in older women.Arch Intern Med.2003;163:949957.
  27. Simon SR,Chan KA,Soumerai SB, et al.Potentially inappropriate medication use by elderly persons in U.S. Health Maintenance Organizations, 2000–2001.J Am Geriatr Soc.2005;53:227232.
  28. Goulding MR.Inappropriate medication prescribing for elderly ambulatory care patients.Arch Intern Med.2004;164:305312.
  29. Passarelli MC,Jacob‐Filho W,Figueras A.Adverse drug reactions in an elderly hospitalised population: inappropriate prescription is a leading cause.Drugs Aging.2005;22:767777.
  30. Raivio MM,Laurila JV,Strandberg TE,Tilvis RS,Pitkala KH.Use of inappropriate medications and their prognostic significance among in‐hospital and nursing home patients with and without dementia in Finland.Drugs Aging.2006;23:333343.
  31. Fialova D,Topinkova E,Gambassi G, et al.Potentially inappropriate medication use among elderly home care patients in Europe.JAMA.2005;293:13481358.
  32. Laroche ML,Charmes JP,Nouaille Y,Fourrier A,Merle L.Impact of hospitalisation in an acute medical geriatric unit on potentially inappropriate medication use.Drugs Aging.2006;23(1):4959.
  33. Brown BK,Earnhart J.Pharmacists and their effectiveness in ensuring the appropriateness of the chronic medication regimens of geriatric inpatients.Consult Pharm.2004;19:432436.
  34. Passaro A,Volpato S,Romagnoni F,Manzoli N,Zuliani G,Fellin R.Benzodiazepines with different half‐life and falling in a hospitalized population: the GIFA study. Gruppo Italiano di Farmacovigilanza nell'Anziano.J Clin Epidemiol.2000;53:12221229.
  35. Gales BJ,Menard SM.Relationship between the administration of selected medications and falls in hospitalized elderly patients.Ann Pharmacother.1995;29:354358.
  36. Mendelson WB.The use of sedative/hypnotic medication and its correlation with falling down in the hospital.Sleep.1996;19:698701.
  37. Wagner AK,Zhang F,Soumerai SB, et al.Benzodiazepine use and hip fractures in the elderly: who is at greatest risk?Arch Intern Med.2004;164:15671572.
  38. Wang PS,Bohn RL,Glynn RJ,Mogun H,Avorn J.Hazardous benzodiazepine regimens in the elderly: effects of half‐life, dosage, and duration on risk of hip fracture.Am J Psychiatry.2001;158:892898.
  39. Tamblyn R,Abrahamowicz M,du Berger R,McLeod P,Bartlett G.A 5‐year prospective assessment of the risk associated with individual benzodiazepines and doses in new elderly users.J Am Geriatr Soc.2005;53:233241.
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Journal of Hospital Medicine - 3(2)
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Journal of Hospital Medicine - 3(2)
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91-102
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91-102
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Potentially inappropriate medication use in hospitalized elders
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Potentially inappropriate medication use in hospitalized elders
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drug safety, geriatric patient, pharmaceuticals, quality improvement
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drug safety, geriatric patient, pharmaceuticals, quality improvement
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