Rough Draft Qualitative Research Critique and Ethical Considerations

ORIGINAL RESEARCH

Contribution of Psychiatric Illness and Substance Abuse to 30-Day Readmission Risk

Robert E. Burke, MD1,2*, Jacques Donz�e, MD, MSc3,4, Jeffrey L. Schnipper, MD, MPH3,4,5

1Hospital Medicine Section, Department of Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, Colorado; 2Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Denver, Colorado; 3Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts; 4Department of Medicine, Harvard Medical School, Boston, Massachusetts; 5Brigham and Women’s Hospital (BWH) Hospitalist Service, Boston, Massachusetts.

BACKGROUND: Little is known about the contribution of psychiatric illness to medical 30-day readmission risk.

OBJECTIVE: To determine the independent contribution of psychiatric illness and substance abuse to all-cause and potentially avoidable 30-day readmissions in medical patients.

DESIGN: Retrospective cohort study.

SETTING: Patients discharged from the medicine services at a large teaching hospital from July 1, 2009 to June 30, 2010.

MEASUREMENTS: The main outcome of interest was 30- day all-cause and potentially avoidable readmissions; the latter determined by a validated algorithm (SQLape) in both bivariate and multivariate analysis. Readmissions were cap- tured at 3 hospitals where the majority of these patients are readmitted.

RESULTS: Of 6987 discharged patients, 1260 were read- mitted within 30 days (18.0%); 388 readmissions were potentially avoidable (5.6%). In multivariate analysis, 2 or

more prescribed outpatient psychiatric medications (odds ratio [OR]: 1.1, 95% confidence interval [CI]: 1.01-1.20) or any prescription of anxiolytics (OR: 1.16, 95% CI: 1.00– 1.35) were associated with increased all-cause readmis- sions, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68-0.99) or substance abuse (OR: 0.80, 96% CI: 0.65-0.99) were associated with fewer all-cause readmis- sions. These findings were not replicated as predictors of potentially avoidable readmissions; rather, patients with dis- charge diagnoses of depression (OR: 1.49, 95% CI: 1.09- 2.04) and schizophrenia (OR: 2.63, 95% CI: 1.13-6.13) were at highest risk.

CONCLUSIONS: Our data suggest that patients treated during a hospitalization for depression and for schizophre- nia are at higher risk for potentially avoidable 30-day read- missions, whereas those prescribed more psychiatric medications as outpatients are at increased risk for all-cause readmissions. These populations may represent fruitful targets for interventions to reduce readmission risk. Journal of Hospital Medicine 2013;8:450–455. VC 2013 Society of Hospital Medicine

Readmissions to the hospital are common and costly.1

However, identifying patients prospectively who are likely to be readmitted and who may benefit from interventions to reduce readmission risk has proven challenging, with published risk scores having only moderate ability to discriminate between patients likely and unlikely to be readmitted.2 One reason for this may be that published studies have not typically focused on patients who are cognitively impaired, psy- chiatrically ill, have low health or English literacy, or have poor social supports, all of whom may represent a substantial fraction of readmitted patients.2–5

Psychiatric disease, in particular, may contribute to increased readmission risk for nonpsychiatric (medi- cal) illness, and is associated with increased utilization

of healthcare resources.6–11 For example, patients with mental illness who were discharged from New York hospitals were more likely to be rehospitalized and had more costly readmissions than patients with- out these comorbidities, including a length of stay nearly 1 day longer on average.7 An unmet need for treatment of substance abuse was projected to cost Tennessee $772 million of excess healthcare costs in 2000, mostly incurred through repeat hospitalizations and emergency department (ED) visits.10

Despite this, few investigators have considered the role of psychiatric disease and/or substance abuse in medical readmission risk. The purpose of the current study was to evaluate the role of psychiatric illness and substance abuse in unselected medical patients to determine their relative contributions to 30-day all- cause readmissions (ACR) and potentially avoidable readmissions (PAR).

METHODS Patients and Setting

We conducted a retrospective cohort study of consecu- tive adult patients discharged from medicine services at Brigham and Women’s Hospital (BWH), a 747-bed tertiary referral center and teaching hospital, between

*Address for correspondence and reprint requests: Robert E. Burke, MD, Denver VA Medical Center, Medical Service (111), 1055 Clermont Street, Denver, CO 80220-3808; Telephone: 303-399-8020; Fax: 303- 393-5199; E-mail: [email protected]

Additional Supporting Information may be found in the online version of this article.

Received: November 8, 2012; Revised: February 27, 2013; Accepted: March 7, 2013 2013 Society of Hospital Medicine DOI 10.1002/jhm.2044 Published online in Wiley Online Library (Wileyonlinelibrary.com).

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July 1, 2009 and June 30, 2010. Most patients are cared for by resident housestaff teams at BWH (approximately 25% are cared for by physician assis- tants working directly with attending physicians), and approximately half receive primary care in the Part- ners system, which has a shared electronic medical record (EMR). Outpatient mental health services are provided by Partners-associated mental health profes- sionals including those at McLean Hospital and MassHealth (Medicaid)-associated sites through the Massachusetts Behavioral Health Partnership. Exclu- sion criteria were death in the hospital or discharge to another acute care facility. We also excluded patients who left against medical advice (AMA). The study protocol was approved by the Partners Institutional Review Board.

Outcome

The primary outcomes were ACR and PAR within 30 days of discharge. First, we identified all 30-day read- missions to BWH or to 2 other hospitals in the Part- ners Healthcare Network (previous studies have shown that 80% of all readmitted patients are read- mitted to 1 of these 3 hospitals).12 For patients with multiple readmissions, only the first readmission was included in the dataset.

To find potentially avoidable readmissions, adminis- trative and billing data for these patients were proc- essed using the SQLape (SQLape s.a.r.l., Corseaux, Switzerland) algorithm, which identifies PAR by excluding patients who undergo planned follow-up treatment (such as a cycle of planned chemotherapy) or are readmitted for conditions unrelated in any way to the index hospitalization.13,14 Common complica- tions of treatment are categorized as “potentially avoidable,” such as development of a deep venous thrombosis, a decubitus ulcer after prolonged bed rest, or bleeding complications after starting anticoa- gulation. Although the algorithm identifies theoreti- cally preventable readmissions, the algorithm does not quantify how preventable they are, and these are thus referred to as “potentially avoidable.” This is similar to other admission metrics, such as the Agency for Healthcare Research and Quality’s prevention quality indicators, which are created from a list of ambulatory care-sensitive conditions.15 SQLape has the advantage of being a specific tool for readmis- sions. Patients with 30-day readmissions identified by SQLape as planned or unlikely to be avoidable were excluded in the PAR analysis, although still included in ACR analysis. In each case, the comparison group is patients without any readmission.

Predictors

Our predictors of interest included the overall preva- lence of a psychiatric diagnosis or diagnosis of sub- stance abuse, the presence of specific psychiatric diagnoses, and prescription of psychiatric medications

to help assess the independent contribution of these comorbidities to readmission risk.

We used a combination of easily obtainable inpa- tient and outpatient clinical and administrative data to identify relevant patients. Patients were considered likely to be psychiatrically ill if they: (1) had a psychi- atric diagnosis on their Partners outpatient EMR problem list and were prescribed a medication to treat that condition as an outpatient, or (2) had an Interna- tional Classification of Diseases, 9th Revision diagno- sis of a psychiatric illness at hospital discharge. Patients were considered to have moderate probability of disease if they: (1) had a psychiatric diagnosis on their outpatient problem list, or (2) were prescribed a medication intended to treat a psychiatric condition as an outpatient. Patients were considered unlikely to have psychiatric disease if none of these criteria were met. Patients were considered likely to have a sub- stance abuse disorder if they had this diagnosis on their outpatient EMR, or were prescribed a medica- tion to treat this condition (eg, buprenorphine/ naloxone), or received inpatient consultation from a substance abuse treatment team during their inpatient hospitalization, and were considered unlikely if none of these were true. We also evaluated individual cate- gories of psychiatric illness (schizophrenia, depression, anxiety, bipolar disorder) and of psychotropic medica- tions (antidepressants, antipsychotics, anxiolytics).

Potential Confounders

Data on potential confounders, based on prior litera- ture,16,17 collected at the index admission were derived from electronic administrative, clinical, and billing sources, including the Brigham Integrated Computer System and the Partners Clinical Data Re- pository. They included patient age, gender, ethnicity, primary language, marital status, insurance status, liv- ing situation prior to admission, discharge location, length of stay, Elixhauser comorbidity index,18 total number of medications prescribed, and number of prior admissions and ED visits in the prior year.

Statistical Analysis

Bivariate comparisons of each of the predictors of ACR and PAR risk (ie, patients with a 30-day ACR or PAR vs those not readmitted within 30 days) were conducted using v2 trend tests for ordinal predictors (eg, likelihood of psychiatric disease), and v2 or Fisher exact test for dichotomous predictors (eg, receipt of inpatient substance abuse counseling).

We then used multivariate logistic regression analy- sis to adjust for all of the potential confounders noted above, entering each variable related to psychiatric ill- ness into the model separately (eg, likely psychiatric illness, number of psychiatric medications). In a sec- ondary analysis, we removed potentially collinear var- iables from the final model; as this did not alter the results, the full model is presented. We also conducted

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a secondary analysis where we included patients who left against medical advice (AMA), which also did not alter the results. Two-sided P values <0.05 were consid- ered significant, and all analyses were performed using the SAS version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS There were 7984 unique patients discharged during the study period. Patients were generally white and English speaking; just over half of admissions came from the ED (Table 1). Of note, nearly all patients were insured, as are almost all patients in Massachu- setts. They had high degrees of comorbid illness and large numbers of prescribed medications. Nearly 30% had at least 1 hospital admission within the prior year.

All-Cause Readmissions

After exclusion of 997 patients who died, were dis- charged to skilled nursing or rehabilitation facilities, or left AMA, 6987 patients were included (Figure 1). Of these, 1260 had a readmission (18%). Approxi- mately half were considered unlikely to be psychiatri- cally ill, 22% were considered moderately likely, and 29% likely (Table 2).

In bivariate analysis (Table 2), likelihood of psychi- atric illness (P < 0.01) and increasing numbers of pre- scribed outpatient psychiatric medications (P < 0.01) were significantly associated with ACR. In multivari- ate analysis, each additional prescribed outpatient psy- chiatric medication increased ACR risk (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.01-1.20) or any prescription of an anxiolytic in particular (OR: 1.16, 95% CI: 1.00–1.35) was associated with increased risk of ACR, whereas discharge diagnoses of anxiety (OR: 0.82, 95% CI: 0.68-0.99) and substance abuse (OR: 0.80, 95% CI: 0.65-0.99) were associated with lower risk of ACR (Table 3).

Potentially Avoidable Readmissions

After further exclusion of 872 patients who had unavoidable readmissions according to the SQLape algorithm, 6115 patients remained. Of these, 388 had a PAR within 30 days (6.3%, Table 1).

In bivariate analysis (Table 2), the likelihood of psychiatric illness (P 5 0.02), number of outpatient psychiatric medications (P 5 0.04), and prescription of anxiolytics (P 5 0.01) were significantly associated with PAR, as they were with ACR. A discharge diag- nosis of schizophrenia was also associated with PAR (P 5 0.03).

In multivariate analysis, only discharge diagnoses of depression (OR: 1.49, 95% CI: 1.09-2.04) and schizo- phrenia (OR: 2.63, 95% CI: 1.13-6.13) were associ- ated with PAR.

DISCUSSION Comorbid psychiatric illness was common among patients admitted to the medicine wards. Patients with

documented discharge diagnoses of depression or schizophrenia were at highest risk for a potentially avoidable 30-day readmission, whereas those pre- scribed more psychiatric medications were at increased risk for ACR. These findings were

TABLE 1. Baseline Characteristics of the Study Population

Characteristic

All

Patients,

N (%)

Not

Readmitted,

N (%)

ACR,

N (%)

PAR

N (%)*

Study cohort 6987 (100) 5727 (72) 1260 (18) 388 (5.6) Age, y <50 1663 (23.8) 1343 (23.5) 320 (25.4) 85 (21.9) 51–65 2273 (32.5) 1859 (32.5) 414 (32.9) 136 (35.1) 66–79 1444 (20.7) 1176 (20.5) 268 (18.6) 80 (20.6) >80 1607 (23.0) 1349 (23.6) 258 (16.1) 87 (22.4)

Female 3604 (51.6) 2967 (51.8) 637 (50.6) 206 (53.1) Race

White 5126 (73.4) 4153 (72.5) 973 (77.2) 300 (77.3) Black 1075 (15.4) 899 (15.7) 176 (14.0) 53 (13.7) Hispanic 562 (8.0) 477 (8.3) 85 (6.8) 28 (7.2) Other 224 (3.2) 198 (3.5) 26 (2.1) 7 (1.8)

Primary language English 6345 (90.8) 5180 (90.5) 1165 (92.5) 356 (91.8)

Marital status Married 3642 (52.1) 2942 (51.4) 702 (55.7) 214 (55.2) Single, never married 1662 (23.8) 1393 (24.3) 269 (21.4) 73 (18.8) Previously married† 1683 (24.1) 1386 (24.2) 289 (22.9) 101 (26.0)

Insurance Medicare 3550 (50.8) 2949 (51.5) 601 (47.7) 188 (48.5) Medicaid 539 (7.7) 430 (7.5) 109 (8.7) 33 (8.5) Private 2892 (41.4) 2344 (40.9) 548 (43.5) 167 (43.0) Uninsured 6 (0.1) 4 (0.1) 2 (0.1) 0 (0)

Source of index admission Clinic or home 2136 (30.6) 1711 (29.9) 425 (33.7) 117 (30.2) Emergency department 3592 (51.4) 2999 (52.4) 593 (47.1) 181 (46.7) Nursing facility 1204 (17.2) 977 (17.1) 227 (18.0) 84 (21.7) Other 55 (0.1) 40 (0.7) 15 (1.1) 6 (1.6)

Length of stay, d 0–2 1757 (25.2) 1556 (27.2) 201 (16.0) 55 (14.2) 3–4 2200 (31.5) 1842 (32.2) 358 (28.4) 105 (27.1) 5–7 1521 (21.8) 1214 (21.2) 307 (24.4) 101 (26.0) >7 1509 (21.6) 1115 (19.5) 394 (31.3) 127 (32.7)

Elixhauser comorbidity index score 0–1 1987 (28.4) 1729 (30.2) 258 (20.5) 66 (17.0) 2–7 1773 (25.4) 1541 (26.9) 232 (18.4) 67 (17.3) 8–13 1535 (22.0) 1212 (21.2) 323 (25.6) 86 (22.2) >13 1692 (24.2) 1245 (21.7) 447 (35.5) 169 (43.6)

Medications prescribed as outpatient 0–6 1684 (24.1) 1410 (24.6) 274 (21.8) 72 (18.6) 7–9 1601 (22.9) 1349 (23.6) 252 (20.0) 77 (19.9) 10–13 1836 (26.3) 1508 (26.3) 328 (26.0) 107 (27.6) >13 1866 (26.7) 1460 (25.5) 406 (32.2) 132 (34.0)

Number of admissions in past year 0 4816 (68.9) 4032 (70.4) 784 (62.2) 279 (71.9) 1–5 2075 (29.7) 1640 (28.6) 435 (34.5) 107 (27.6) >5 96 (1.4) 55 (1.0) 41 (3.3) 2 (0.5)

Number of ED visits in past year 0 4661 (66.7) 3862 (67.4) 799 (63.4) 261 (67.3) 1–5 2326 (33.3) 1865 (32.6) 461 (36.6) 127 (32.7)

NOTE: Abbreviations: ACR, all-cause readmission; ED, emergency department; PAR, potentially avoidable readmission. PAR cohort excludes patients with unavoidable readmissions. *Percentages may not add up to 100% due to rounding or when subcategories were very small (<0.5%). †Previously married includes patients who were divorced or widowed.

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independent of a comprehensive set of risk factors among medicine inpatients in this retrospective cohort study.

This study extends prior work indicating patients with psychiatric disease have increased healthcare uti- lization,6–11 by identifying at least 2 subpopulations of the psychiatrically ill (those with depression and schizophrenia) at particularly high risk for 30-day PAR. To our knowledge, this is the first study to iden- tify schizophrenia as a predictor of hospital readmis- sion for medical illnesses. One prior study prospectively identified depression as increasing the 90-day risk of readmission 3-fold, although medica- tion usage was not assessed,6 and our report strength- ens this association.

There are several possible explanations why these two subpopulations in particular would be more pre- disposed to readmissions that are potentially avoid- able. It is known that patients with schizophrenia, for example, live on average 20 years less than the general population, and most of this excess mortality is due to medical illnesses.19,20 Reasons for this may

include poor healthcare access, adverse effects of medication, and socioeconomic factors among others.21,22 All of these reasons may contribute to the increased PAR risk in this population, mediated, for example, by decreased ability to adhere to postdischarge care plans. Successful community- based interventions to decrease these inequities have been described and could serve as a model for addressing the increased readmission risk in this population.23

Our finding that patients with a greater number of prescribed psychiatric medications are at increased risk for ACR may be expected, given other studies that have highlighted the crucial importance of medi- cations in postdischarge adverse events, including readmissions.24 Indeed, medication-related errors and toxicities are the most common postdischarge adverse events experienced by patients.25 Whether psychiatric medications are particularly prone to causing postdi- scharge adverse events or whether these medications represent greater psychiatric comorbidity cannot be answered by this study.

TABLE 2. Bivariate Analysis of Predictors of Readmission Risk

All-Cause Readmission Analysis Potentially Avoidable Readmission Analysis

No. in Cohort (%) % of Patients With ACR P Value* No. in Cohort (%) % of Patients With PAR P Value*

Entire cohort 6987 18.0 6115 6.3 Likelihood of psychiatric illness

Unlikely 3424 (49) 16.5 3026 (49) 5.6 Moderate 1564 (22) 23.5 1302 (21) 7.1 Likely 1999 (29) 16.4 1787 (29) 6.4 Likely versus unlikely 0.87 0.20 Moderate 1 likely versus unlikely 0.001 0.02

Likelihood of substance abuse 0.01 0.20 Unlikely 5804 (83) 18.7 5104 (83) 6.5 Likely 1183 (17) 14.8 1011 (17) 5.4 0.14

Number of prescribed outpatient psychotropic medications <0.001 0.04 0 4420 (63) 16.3 3931 (64) 5.9 1 1725 (25) 20.4 1481 (24) 7.2 2 781 (11) 22.3 653 (11) 7.0 >2 61 (1) 23.0 50 (1) 6.0

Prescribed antidepressant 1474 (21) 20.6 0.005 1248 (20) 6.2 0.77 Prescribed antipsychotic 375 (5) 22.4 0.02 315 (5) 7.6 0.34 Prescribed mood stabilizer 81 (1) 18.5 0.91 69 (1) 4.4 0.49 Prescribed anxiolytic 1814 (26) 21.8 <0.001 1537 (25) 7.7 0.01 Prescribed stimulant 101 (2) 26.7 0.02 83 (1) 10.8 0.09 Prescribed pharmacologic treatment for substance abuse 79 (1) 25.3 0.09 60 (1) 1.7 0.14 Number of psychiatric diagnoses on outpatient problem list 0.31 0.74

0 6405 (92) 18.2 5509 (90) 6.3 1 or more 582 (8) 16.5 474 (8) 7.0

Outpatient diagnosis of substance abuse 159 (2) 13.2 0.11 144 (2) 4.2 0.28 Outpatient diagnosis of any psychiatric illness 582 (8) 16.5 0.31 517 (8) 8.0 0.73 Discharge diagnosis of depression 774 (11) 17.7 0.80 690 (11) 7.7 0.13 Discharge diagnosis of schizophrenia 56 (1) 23.2 0.31 50 (1) 14 0.03 Discharge diagnosis of bipolar disorder 101 (1) 10.9 0.06 92 (2) 2.2 0.10 Discharge diagnosis of anxiety 1192 (17) 15.0 0.003 1080 (18) 6.2 0.83 Discharge diagnosis of substance abuse 885 (13) 14.8 0.008 803 (13) 6.1 0.76 Discharge diagnosis of any psychiatric illness 1839 (26) 16.0 0.008 1654 (27) 6.6 0.63 Substance abuse consultation as inpatient 284 (4) 14.4 0.11 252 (4) 3.6 0.07

NOTE: Abbreviations: ACR, all-cause readmission, PAR, potentially avoidable readmission. *All analyses performed with v2 trend test for ordinal variables in more than 2 categories or Fisher exact test for dichotomous variables. Comparison group is patients without a readmission in all analyses. PAR analysis excludes patients with nonpreventable readmissions as determined by the SQLape algorithm.

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It was surprising but reassuring that substance abuse was not a predictor of short-term readmissions as identified using our measures; in fact, a discharge diagnosis of substance abuse was associated with lower risk of ACR than comparator patients. It seems unlikely that we would have inadequate power to find such a result, as we found a statistically significant negative association in the ACR population, and 17% of our population overall was considered likely to have a substance abuse comorbidity. However, it is likely the burden of disease was underestimated given that we did not try to determine the contribution of long-term substance abuse to medical diseases that may increase readmission risk (eg, liver cirrhosis from alcohol use). Unlike other conditions in our study, patients with substance abuse diagnoses at BWH can be seen by a dedicated multidisciplinary team while an inpatient to start treatment and plan for postdi- scharge follow-up; this may have played a role in our findings.

A discharge diagnosis of anxiety was also somewhat protective against readmission, whereas a prescription of an anxiolytic (predominantly benzodiazepines) increased risk; many patients prescribed a benzodiaze- pine do not have a Diagnostic and Statistical Manual of Mental Disorders–4th Edition (DSM-IV) diagnosis of anxiety disorder, and thus these findings may reflect different patient populations. Discharging physicians may have used anxiety as a discharge diagnosis in patients in whom they suspected somatic complaints without organic basis; these patients may be at lower risk of readmission.

Discharge diagnoses of psychiatric illnesses were associated with ACR and PAR in our study, but out- patient diagnoses were not. This likely reflects greater severity of illness (documentation as a treated diagno- sis on discharge indicates the illness was relevant dur- ing the hospitalization), but may also reflect inaccuracies of diagnosis and lack of assessment of se- verity in outpatient coding, which would bias toward null findings. Although many of the patients in our study were seen by primary care doctors within the Partners system, some patients had outside primary care physicians and we did not have access to these records. This may also have decreased our ability to find associations.

The findings of our study should be interpreted in the context of the study design. Our study was retro- spective, which limited our ability to conclusively diagnose psychiatric disease presence or severity (as is true of most institutions, validated psychiatric screen- ing was not routinely used at our institutions on hos- pital admission or discharge). However, we used a conservative scale to classify the likelihood of patients having psychiatric or substance abuse disorders, and we used other metrics to establish the presence of ill- ness, such as the number of prescribed medications, inpatient consultation with a substance abuse service, and hospital discharge diagnoses. This approach also allowed us to quickly identify a large cohort unaf- fected by selection bias. Our study was single center, potentially limiting generalizability. Although we cap- ture at least 80% of readmissions, we were not able to capture all readmissions, and we cannot rule out that patients readmitted elsewhere are different than those readmitted within the Partners system. Last, the SQLape algorithm is not perfectly sensitive or specific in identifying avoidable readmissions,13 but it does eliminate many readmissions that are clearly unavoid- able, creating an enriched cohort of patients whose readmissions are more likely to be avoidable and therefore potentially actionable.

We suggest that our study findings first be consid- ered when risk stratifying patients before hospital dis- charge in terms of readmission risk. Patients with depression and schizophrenia would seem to merit postdischarge interventions to decrease their poten- tially avoidable readmissions. Compulsory community treatment (a feature of treatment in Canada and Aus- tralia that is ordered by clinicians) has been shown to decrease mortality due to medical illness in patients who have been hospitalized and are psychiatrically ill, and addition of these services to postdischarge care may be useful.23 Inpatient physicians could work to ensure follow-up not just with medical providers but with robust outpatient mental health programs to decrease potentially avoidable readmission risk, and administrators could work to ensure close linkages with these community resources. Studies evaluating the impact of these types of interventions would need

TABLE 3. Multivariate Analysis of Predictors of Readmission Risk

ACR, OR

(95% CI)

PAR, OR

(95% CI)*

Likely psychiatric disease 0.97 (0.82-1.14) 1.20 (0.92-1.56) Likely and possible psychiatric disease 1.07 (0.94-1.22) 1.18 (0.94-1.47) Likely substance abuse 0.83 (0.69-0.99) 0.85 (0.63-1.16) Psychiatric diagnosis on outpatient problem list 0.97 (0.76-1.23) 1.04 (0.70-1.55) Substance abuse diagnosis on outpatient problem list 0.63 (0.39-1.02) 0.65 (0.28-1.52) Increasing number of prescribed psychiatric medications 1.10 (1.01-1.20) 1.00 (0.86-1.16)

Outpatient prescription for antidepressant 1.10 (0.94-1.29) 0.86 (0.66-1.13) Outpatient prescription for antipsychotic 1.03 (0.79-1.34) 0.93 (0.59-1.45) Outpatient prescription for anxiolytic 1.16 (1.00–1.35) 1.13 (0.88-1.44) Outpatient prescription for methadone or buprenorphine 1.15 (0.67-1.98) 0.18 (0.03-1.36)

Discharge diagnosis of depression 1.06 (0.86-1.30) 1.49 (1.09-2.04) Discharge diagnosis of schizophrenia 1.43 (0.75-2.74) 2.63 (1.13-6.13) Discharge diagnosis of bipolar disorder 0.53 (0.28-1.02) 0.35 (0.09-1.45) Discharge diagnosis of anxiety 0.82 (0.68-0.99) 1.11 (0.83-1.49) Discharge diagnosis of substance abuse 0.80 (0.65-0.99) 1.05 (0.75-1.46) Discharge diagnosis of any psychiatric illness 0.88 (0.75-1.02) 1.22 (0.96-1.56) Addiction team consult while inpatient 0.82 (0.58-1.17) 0.58 (0.29-1.17)

NOTE: Abbreviations: ACR, all-cause readmissions; CI, confidence interval; OR, odds ratio; PAR, poten- tially avoidable readmissions. *All analyses performed by multivariate logistic regression adjusting for patient age, gender, ethnicity, language spoken, marital status, insurance source, discharge location, length of stay, comorbidities (Elixhauser), number of outpatient medications, number of prior emergency depart- ment visits, and admissions in the prior year. Analyses were performed by entering each exposure of interest into the model separately while adjusting for all covariates. Comparison group is patients without any read- mission for all analyses.

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to be conducted. Patients with polypharmacy, includ- ing psychiatric medications, may benefit from inter- ventions to improve medication safety, such as enhanced medication reconciliation and pharmacist counseling.26

Our study suggests that patients with depression, those with schizophrenia, and those who have increased numbers of prescribed psychiatric medica- tions should be considered at high risk for readmis- sion for medical illnesses. Targeting interventions to these patients may be fruitful in preventing avoidable readmissions.

Acknowledgements The authors thank Dr. Yves Eggli for screening the database for poten- tially avoidable readmissions using the SQLape algorithm.

Disclosures: Dr. Donz�e was supported by the Swiss National Science Foundation and the Swiss Foundation for Medical–Biological Scholar- ships. The authors otherwise have no conflicts of interest to disclose. The content is solely the responsibility of the authors and does not nec- essarily represent the official views of the US Department of Veterans Affairs.

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