Predicting Psychiatric Hospitalizations among Elderly Veterans with a History of Mental Health Disease. Issue 1 (17th May 2018)

• Record Type: Journal Article
• Authors: Burningham, Zachary; Leng, Jianwei; Peters, Celena B.; Huynh, Tina; Halwani, Ahmad; Rupper, Randall; Hicken, Bret; Sauer, Brian C.
• Is Part Of: EGEMS. Volume 6:Issue 1(2018)
• Publication Date: 2018-05-17
• Subjects: Health Services Research -- Quality Improvement -- Decision Support Techniques -- Risk Factors -- Mental Health -- Veterans; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.207
• Languages: English
• ISSNs: 2327-9214

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.