Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System

Ronald C Kessler, Mark S Bauer, Todd M Bishop, Olga V Demler, Steven K Dobscha, Sarah M Gildea, Joseph L Goulet, Elizabeth Karras, Julie Kreyenbuhl, Sara J Landes, Howard Liu, Alex R Luedtke, Patrick Mair, William H B McAuliffe, Matthew Nock, Maria Petukhova, Wilfred R Pigeon, Nancy A Sampson, Jordan W Smoller, Lauren M Weinstock, Robert M Bossarte, Ronald C Kessler, Mark S Bauer, Todd M Bishop, Olga V Demler, Steven K Dobscha, Sarah M Gildea, Joseph L Goulet, Elizabeth Karras, Julie Kreyenbuhl, Sara J Landes, Howard Liu, Alex R Luedtke, Patrick Mair, William H B McAuliffe, Matthew Nock, Maria Petukhova, Wilfred R Pigeon, Nancy A Sampson, Jordan W Smoller, Lauren M Weinstock, Robert M Bossarte

Abstract

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

Keywords: intensive case management; machine learning; predictive analytics; suicide; super learner.

Copyright © 2020 Kessler, Bauer, Bishop, Demler, Dobscha, Gildea, Goulet, Karras, Kreyenbuhl, Landes, Liu, Luedtke, Mair, McAuliffe, Nock, Petukhova, Pigeon, Sampson, Smoller, Weinstock and Bossarte.

Figures

Figure 1
Figure 1
Monthly suicide hazard rates and cumulative incidence rates over the 12 months after psychiatric hospital discharge in (A) the training sample (January 1, 2010–October 22, 2012) and (B) holdout sample (October 23, 2012–December 31, 2013).
Figure 2
Figure 2
Receiver operating characteristic (ROC) curve for the best Super Learner model (to predict suicides within 12 months of hospital discharge) applied in the holdout sample to predict suicides over each of the five time horizons.

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