Journal of Psychiatric Research 61 (2015) 205e213
Contents lists available at ScienceDirect
Journal of Psychiatric Research journal homepage: www.elsevier.com/locate/psychires
READMIT: A clinical risk index to predict 30-day readmission after discharge from acute psychiatric units* Simone N. Vigod a, b, c, d, *, Paul A. Kurdyak c, d, e, Dallas Seitz f, Nathan Herrmann d, g, Kinwah Fung c, Elizabeth Lin c, d, e, Christopher Perlman h, Valerie H. Taylor a, b, c, d, Paula A. Rochon a, b, c, d, Andrea Gruneir a, b, c, d, i a
Women's College Hospital, 76 Grenville Street, Toronto, Ontario, Canada Women's College Research Institute, 790 Bay Street, Toronto, Ontario, Canada Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario, Canada d University of Toronto, 27 King's College Circle, Toronto, Ontario, Canada e Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario, Canada f Queens University, 99 University Avenue, Kingston, Ontario, Canada g Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario, Canada h University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada i University of Alberta, 6-40 University Terrace, Edmonton, Alberta, Canada b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 August 2014 Received in revised form 28 October 2014 Accepted 4 December 2014
Our aim was to create a clinically useful risk index, administered prior to discharge, for determining the probability of psychiatric readmission within 30 days of hospital discharge for general psychiatric inpatients. We used population-level sociodemographic and health administrative data to develop a predictive model for 30-day readmission among adults discharged from an acute psychiatric unit in Ontario, Canada (2008e2011), and converted the final model into a risk index system. We derived the predictive model in one-half of the sample (n ¼ 32,749) and validated it in the other half of the sample (n ¼ 32,750). Variables independently associated with 30-day readmission (forming the mnemonic READMIT) were: (R) Repeat admissions; (E) Emergent admissions (i.e. harm to self/others); (D) Diagnoses (psychosis, bipolar and/or personality disorder), and unplanned Discharge; (M) Medical comorbidity; (I) prior service use Intensity; and (T) Time in hospital. Each 1-point increase in READMIT score (range 0e41) increased the odds of 30-day readmission by 11% (odds ratio 1.11, 95% CI 1.10e1.12). The index had moderate discriminative capacity in both derivation (C-statistic ¼ 0.631) and validation (C-statistic ¼ 0.630) datasets. Determining risk of psychiatric readmission for individual patients is a critical step in efforts to address the potentially avoidable high rate of this negative outcome. The READMIT index provides a framework for identifying patients at high risk of 30-day readmission prior to discharge, and for the development, evaluation and delivery of interventions that can assist with optimizing the transition to community care for patients following psychiatric discharge. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Psychiatric readmission Psychiatric epidemiology Risk index
1. Introduction Worldwide, almost 1 in 7 individuals hospitalized for psychiatric reasons are readmitted within 30 days of discharge (Leslie and
* These results were presented at the Canadian Psychiatric Association meeting in Toronto, Ontario in September 2014. * Corresponding author. Department of Psychiatry, Women's College Hospital, 76 Grenville St. Rm. 7234, Toronto, Ontario, M5S 1B2, Canada. Tel.: þ1 416 323 6400x4080; fax: þ1 416 323 6356. E-mail address: [email protected] (S.N. Vigod).
http://dx.doi.org/10.1016/j.jpsychires.2014.12.003 0022-3956/© 2014 Elsevier Ltd. All rights reserved.
Rosenheck, 2000; Canadian Institute for Health Information and Statistics Canada, 2011; National Association of State Mental Health Program Directors Research Institute, 2012; OECD, 2013). This high readmission rate is a negative outcome from a clinical and public health perspective (Canadian Institute for Health Information and Statistics Canada, 2011), and is highly disruptive to patients and their families. It is also, at least to some extent, an avoidable outcome as supported by evidence that: (1) interventions of varying types have been evaluated in clinical trials and shown to reduce early readmission rates (Vigod et al., 2013a); and (2) some mental health care systems that have developed new
206
S.N. Vigod et al. / Journal of Psychiatric Research 61 (2015) 205e213
organizational and delivery models are demonstrating downward trends in readmission rates (OECD, 2013). To apply interventions efficiently and effectively, however, it is important to be able to identify the individuals who would benefit most. A risk score that quantifies the probability of early readmission for an individual patient could be used in a clinical setting to identify individuals who most require intervention; in a research setting to determine the appropriate target populations for specific interventions designed to reduce early readmission; and in hospital and public policy settings to align resources to areas of greatest need. While many studies have identified risk factors for early psychiatric readmission (Hendryx et al., 2003), few have focused on methods to quantify risk of psychiatric readmission for individuals in a clinical setting. Existing studies focus on specific populations (Gearing et al., 2009) such that their findings are not applicable to the majority of general adult inpatient settings that provide treatment to individuals with a range of diagnoses and needs. Our objective was to derive and validate a clinical risk index that predicts an individual's risk of psychiatric readmission within 30 days of hospital discharge from a general psychiatric inpatient setting. The specific intent of the risk index was for it to be used prior to discharge in any general psychiatric setting to help identify people who are at high risk of readmission so that they can receive services and supports during the hospitalization and postdischarge that may reduce that risk. We had access to a wide array of population-based, health administrative data that can be organized broadly into 4 categories: (1) sociodemographic variables; (2) prior health care utilization; (3) basic clinical and administrative information from a hospital admission; and (4) detailed psychiatric rating scales and metrics administered by clinicians during inpatient psychiatric admission. Accordingly, we systematically evaluated the relative contributions of these categories of information that are increasingly complex to measure on readmission risk prediction. This strategy allowed us to determine the added predictive capacity of including information that is more detailed, but would require greater effort and resources to collect. Therefore, in addition to predicting risk of readmission, we were also able to create a risk index that maximized both risk prediction and feasibility of data collection in a clinical setting. 2. Methods
number. The RPDB also contains age, gender and postal code. Index psychiatric admission information was obtained from the Ontario Mental Health Reporting System (OMHRS). OMHRS contains information on all hospital admissions for adults aged 18 and older admitted to psychiatric inpatient beds in Ontario. OMHRS data are derived from the Resident Assessment Instrument e Mental Health (RAI-MH), a comprehensive clinical assessment tool that is completed within 3 days of admission, at 90-day intervals during the admission (where applicable) and at discharge. The RAI-MH contains information on patient demographics, socioeconomic status (e.g. source of income, place of residence prior to admission, marital status), admission and discharge diagnoses according to Diagnostic and Statistical Manual of Mental Disorder, version IV (DSM-IV) criteria, measures of psychiatric symptoms, substance use, cognition and functional impairment (Hirdes et al., 2000). In inpatient settings, the items have adequate reliability (inter-rater agreement > 80%) and validity (Hirdes et al., 2002). Additional information was derived from other linked ICES datasets whose accuracy has been previously described (Williams et al., 1996). The Ontario Health Insurance Program (OHIP) database contains information on all physician outpatient and inpatient visits including procedures and diagnostic codes. The Canadian Institutes of Health Information - Discharge Abstract Database (CIHI-DAD) contains information on all non-OMHRS acute care hospitalizations and the CIHI National Ambulatory Care Reporting System (NACRS) contains information on all emergency room visits. The study received research ethics board approvals from Women's College Hospital and Sunnybrook Health Sciences Centre in Toronto, Ontario (ICES logged study: 2013 0904 301 000). 2.3. Cohort We identified all individuals aged 18 or older who were discharged from an Ontario APU between April 1, 2008 and March 31, 2011 using the OMHRS dataset, selecting an individual's first discharge during the study period as the index admission. Only individuals who were hospitalized for 72 h were included in our cohort because the characteristics of individuals hospitalized for shorter stays differ substantially from the characteristics of individuals who are hospitalized for at least 72 h, and the number of mandatory RAI-MH assessment variables is much lower for admissions
Contents lists available at ScienceDirect
Journal of Psychiatric Research journal homepage: www.elsevier.com/locate/psychires
READMIT: A clinical risk index to predict 30-day readmission after discharge from acute psychiatric units* Simone N. Vigod a, b, c, d, *, Paul A. Kurdyak c, d, e, Dallas Seitz f, Nathan Herrmann d, g, Kinwah Fung c, Elizabeth Lin c, d, e, Christopher Perlman h, Valerie H. Taylor a, b, c, d, Paula A. Rochon a, b, c, d, Andrea Gruneir a, b, c, d, i a
Women's College Hospital, 76 Grenville Street, Toronto, Ontario, Canada Women's College Research Institute, 790 Bay Street, Toronto, Ontario, Canada Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario, Canada d University of Toronto, 27 King's College Circle, Toronto, Ontario, Canada e Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario, Canada f Queens University, 99 University Avenue, Kingston, Ontario, Canada g Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, Ontario, Canada h University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada i University of Alberta, 6-40 University Terrace, Edmonton, Alberta, Canada b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 August 2014 Received in revised form 28 October 2014 Accepted 4 December 2014
Our aim was to create a clinically useful risk index, administered prior to discharge, for determining the probability of psychiatric readmission within 30 days of hospital discharge for general psychiatric inpatients. We used population-level sociodemographic and health administrative data to develop a predictive model for 30-day readmission among adults discharged from an acute psychiatric unit in Ontario, Canada (2008e2011), and converted the final model into a risk index system. We derived the predictive model in one-half of the sample (n ¼ 32,749) and validated it in the other half of the sample (n ¼ 32,750). Variables independently associated with 30-day readmission (forming the mnemonic READMIT) were: (R) Repeat admissions; (E) Emergent admissions (i.e. harm to self/others); (D) Diagnoses (psychosis, bipolar and/or personality disorder), and unplanned Discharge; (M) Medical comorbidity; (I) prior service use Intensity; and (T) Time in hospital. Each 1-point increase in READMIT score (range 0e41) increased the odds of 30-day readmission by 11% (odds ratio 1.11, 95% CI 1.10e1.12). The index had moderate discriminative capacity in both derivation (C-statistic ¼ 0.631) and validation (C-statistic ¼ 0.630) datasets. Determining risk of psychiatric readmission for individual patients is a critical step in efforts to address the potentially avoidable high rate of this negative outcome. The READMIT index provides a framework for identifying patients at high risk of 30-day readmission prior to discharge, and for the development, evaluation and delivery of interventions that can assist with optimizing the transition to community care for patients following psychiatric discharge. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Psychiatric readmission Psychiatric epidemiology Risk index
1. Introduction Worldwide, almost 1 in 7 individuals hospitalized for psychiatric reasons are readmitted within 30 days of discharge (Leslie and
* These results were presented at the Canadian Psychiatric Association meeting in Toronto, Ontario in September 2014. * Corresponding author. Department of Psychiatry, Women's College Hospital, 76 Grenville St. Rm. 7234, Toronto, Ontario, M5S 1B2, Canada. Tel.: þ1 416 323 6400x4080; fax: þ1 416 323 6356. E-mail address: [email protected] (S.N. Vigod).
http://dx.doi.org/10.1016/j.jpsychires.2014.12.003 0022-3956/© 2014 Elsevier Ltd. All rights reserved.
Rosenheck, 2000; Canadian Institute for Health Information and Statistics Canada, 2011; National Association of State Mental Health Program Directors Research Institute, 2012; OECD, 2013). This high readmission rate is a negative outcome from a clinical and public health perspective (Canadian Institute for Health Information and Statistics Canada, 2011), and is highly disruptive to patients and their families. It is also, at least to some extent, an avoidable outcome as supported by evidence that: (1) interventions of varying types have been evaluated in clinical trials and shown to reduce early readmission rates (Vigod et al., 2013a); and (2) some mental health care systems that have developed new
206
S.N. Vigod et al. / Journal of Psychiatric Research 61 (2015) 205e213
organizational and delivery models are demonstrating downward trends in readmission rates (OECD, 2013). To apply interventions efficiently and effectively, however, it is important to be able to identify the individuals who would benefit most. A risk score that quantifies the probability of early readmission for an individual patient could be used in a clinical setting to identify individuals who most require intervention; in a research setting to determine the appropriate target populations for specific interventions designed to reduce early readmission; and in hospital and public policy settings to align resources to areas of greatest need. While many studies have identified risk factors for early psychiatric readmission (Hendryx et al., 2003), few have focused on methods to quantify risk of psychiatric readmission for individuals in a clinical setting. Existing studies focus on specific populations (Gearing et al., 2009) such that their findings are not applicable to the majority of general adult inpatient settings that provide treatment to individuals with a range of diagnoses and needs. Our objective was to derive and validate a clinical risk index that predicts an individual's risk of psychiatric readmission within 30 days of hospital discharge from a general psychiatric inpatient setting. The specific intent of the risk index was for it to be used prior to discharge in any general psychiatric setting to help identify people who are at high risk of readmission so that they can receive services and supports during the hospitalization and postdischarge that may reduce that risk. We had access to a wide array of population-based, health administrative data that can be organized broadly into 4 categories: (1) sociodemographic variables; (2) prior health care utilization; (3) basic clinical and administrative information from a hospital admission; and (4) detailed psychiatric rating scales and metrics administered by clinicians during inpatient psychiatric admission. Accordingly, we systematically evaluated the relative contributions of these categories of information that are increasingly complex to measure on readmission risk prediction. This strategy allowed us to determine the added predictive capacity of including information that is more detailed, but would require greater effort and resources to collect. Therefore, in addition to predicting risk of readmission, we were also able to create a risk index that maximized both risk prediction and feasibility of data collection in a clinical setting. 2. Methods
number. The RPDB also contains age, gender and postal code. Index psychiatric admission information was obtained from the Ontario Mental Health Reporting System (OMHRS). OMHRS contains information on all hospital admissions for adults aged 18 and older admitted to psychiatric inpatient beds in Ontario. OMHRS data are derived from the Resident Assessment Instrument e Mental Health (RAI-MH), a comprehensive clinical assessment tool that is completed within 3 days of admission, at 90-day intervals during the admission (where applicable) and at discharge. The RAI-MH contains information on patient demographics, socioeconomic status (e.g. source of income, place of residence prior to admission, marital status), admission and discharge diagnoses according to Diagnostic and Statistical Manual of Mental Disorder, version IV (DSM-IV) criteria, measures of psychiatric symptoms, substance use, cognition and functional impairment (Hirdes et al., 2000). In inpatient settings, the items have adequate reliability (inter-rater agreement > 80%) and validity (Hirdes et al., 2002). Additional information was derived from other linked ICES datasets whose accuracy has been previously described (Williams et al., 1996). The Ontario Health Insurance Program (OHIP) database contains information on all physician outpatient and inpatient visits including procedures and diagnostic codes. The Canadian Institutes of Health Information - Discharge Abstract Database (CIHI-DAD) contains information on all non-OMHRS acute care hospitalizations and the CIHI National Ambulatory Care Reporting System (NACRS) contains information on all emergency room visits. The study received research ethics board approvals from Women's College Hospital and Sunnybrook Health Sciences Centre in Toronto, Ontario (ICES logged study: 2013 0904 301 000). 2.3. Cohort We identified all individuals aged 18 or older who were discharged from an Ontario APU between April 1, 2008 and March 31, 2011 using the OMHRS dataset, selecting an individual's first discharge during the study period as the index admission. Only individuals who were hospitalized for 72 h were included in our cohort because the characteristics of individuals hospitalized for shorter stays differ substantially from the characteristics of individuals who are hospitalized for at least 72 h, and the number of mandatory RAI-MH assessment variables is much lower for admissions
