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

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

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2020.00390

DOI=10.3389/fpsyt.2020.00390

ISSN=1664-0640

ABSTRACT=There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive post-discharge 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.