Predicting Psychiatric Hospitalizations among Elderly Veterans with a History of Mental Health Disease.

Zachary Burningham,Tina Huynh,Celena B Peters,Jianwei Leng,Randall Rupper,Ahmad Halwani,Bret Hicken,Brian C Sauer

doi:10.5334/egems.207

Abstract

Introduction:Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. However, few resources exist that use real-time information on patient risk to prioritize coordinating appropriate care amongst a complex aging population.Objective:To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease.Methods:This study applied a cohort design to historical data available in the Veterans Affairs (VA) Corporate Data Warehouse (CDW). The Least Absolute Shrinkage and Selection Operator (LASSO) regularization regression technique was used for model development and variable selection. Individual predicted probabilities were estimated using logistic regression. A split-sample approach was used in performing external validation of the fitted model. The concordance statistic (C-statistic) was calculated to assess model performance.Results:Prior to modeling, 61 potential candidate predictors were identified and 27 variables remained after applying the LASSO method. The final model’s predictive accuracy is represented by a C-statistic of 0.903. The model’s predictive accuracy during external validation is represented by a C-statistic of 0.935. Having a previous psychiatric hospitalization, psychosis, bipolar disorder, and the number of mental-health related social work encounters were strong predictors of a geriatric psychiatric hospitalization.Conclusion:This predictive model is capable of quantifying the risk of a geriatric psychiatric hospitalization with acceptable performance and allows for the development of interventions that could potentially reduce such risk.

Highlights

Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises
In order to prevent psychiatric crisis, reduce psychiatric institutionalization, and decrease the burden that PACT care managers and other clinical teams may have in coordinating appropriate care amongst a complex aging population, we have developed and validated a prediction model that identifies Veteran geriatric patients, with existing mental health conditions, who are at risk of experiencing a geriatric psychiatric hospital admission within 90 days
Predictors Intercept Number of emergency department visits in the last three months Number of unique mental health-related diagnoses Care Assessment Needs (CAN) Score: Probability of hospitalization in the 90 days greater than 90% Previous psychiatric hospitalization in the last year Diagnosed with psychosis Diagnosed with bipolar disorder Number of Beers qualified medication fills Diagnosed with dementia Attempted suicide and intentional self-inflicted injury

Summary

Introduction

Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. Objective: To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease. Conclusion: This predictive model is capable of quantifying the risk of a geriatric psychiatric hospitalization with acceptable performance and allows for the development of interventions that could potentially reduce such risk. Aging Veterans are especially vulnerable to institutionalization because they often suffer from both chronic physical and psychiatric conditions, and reportedly experience greater disease burden in comparison to other elderly populations [2, 3]. Older Veterans with multiple comorbid mental conditions and chronic physical conditions are increasingly difficult to treat due to the complexity of multiple disease states [5]. Aging Veterans with psychiatric disease are likely some of the most time-consuming and complex patients a provider must manage

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