Juliet Edgcomb1, Trevor Shaddox2, Gerhard Hellemann2, John O Brooks2. 1. Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA. Electronic address: jedgcomb@mednet.ucla.edu. 2. Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA.
Abstract
BACKGROUND: Individuals with co-existing serious mental illness and non-psychiatric medical illness are at high risk of acute care utilization. Mining of electronic health record data can help identify and categorize predictors of psychiatric hospital readmission in this population. OBJECTIVE: This study aimed to identify modifiable predictors of psychiatric readmission among individuals with comorbid bipolar disorder and medical illness. This goal was accomplished by applying objective variable selection via machine learning techniques. METHOD: This was a retrospective analysis of electronic health record data derived from 77,296 episodes of care from 2006 to 2016 within the University of California Health Care System. Data included 1,250 episodes of care involving patients with bipolar disorder and serious comorbid medical illnesses (defined by transfer between medicine and psychiatry services or concomitant primary medical and psychiatric admission diagnoses). Machine learning (classification trees) was used to identify potential predictors of 30-day psychiatric readmission across hospital encounters. Predictors included demographics, medical and psychiatric diagnoses, medication regimen, and disposition. The algorithm was internally validated using 10-fold cross-validation. RESULTS: The model predicted 30-day readmission with high accuracy (98% unbalanced model, 88% balanced model). Modifiable predictors of readmission were length of stay, transfers between medical and psychiatric services, discharge disposition to home, and all-cause acute health service utilization in the year before the index hospitalization. CONCLUSION: Among bipolar disorder patients with comorbid medical conditions, characteristics of the index hospitalization (e.g., duration, transfer, and disposition) emerged as more predictive than static properties of the patient (e.g., sociodemographic factors and psychiatric comorbidity burden). Findings identified phenotypes of patients at high risk for rehospitalization and suggest potential ways of modifying the risk of early readmission.
BACKGROUND: Individuals with co-existing serious mental illness and non-psychiatric medical illness are at high risk of acute care utilization. Mining of electronic health record data can help identify and categorize predictors of psychiatric hospital readmission in this population. OBJECTIVE: This study aimed to identify modifiable predictors of psychiatric readmission among individuals with comorbid bipolar disorder and medical illness. This goal was accomplished by applying objective variable selection via machine learning techniques. METHOD: This was a retrospective analysis of electronic health record data derived from 77,296 episodes of care from 2006 to 2016 within the University of California Health Care System. Data included 1,250 episodes of care involving patients with bipolar disorder and serious comorbid medical illnesses (defined by transfer between medicine and psychiatry services or concomitant primary medical and psychiatric admission diagnoses). Machine learning (classification trees) was used to identify potential predictors of 30-day psychiatric readmission across hospital encounters. Predictors included demographics, medical and psychiatric diagnoses, medication regimen, and disposition. The algorithm was internally validated using 10-fold cross-validation. RESULTS: The model predicted 30-day readmission with high accuracy (98% unbalanced model, 88% balanced model). Modifiable predictors of readmission were length of stay, transfers between medical and psychiatric services, discharge disposition to home, and all-cause acute health service utilization in the year before the index hospitalization. CONCLUSION: Among bipolar disorderpatients with comorbid medical conditions, characteristics of the index hospitalization (e.g., duration, transfer, and disposition) emerged as more predictive than static properties of the patient (e.g., sociodemographic factors and psychiatric comorbidity burden). Findings identified phenotypes of patients at high risk for rehospitalization and suggest potential ways of modifying the risk of early readmission.
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