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. 2017 Sep;26(3):e1575.
doi: 10.1002/mpr.1575. Epub 2017 Jul 4.

Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration

Ronald C Kessler  1 , Irving Hwang  1 , Claire A Hoffmire  2 , John F McCarthy  3 , Maria V Petukhova  1 , Anthony J Rosellini  4 , Nancy A Sampson  1 , Alexandra L Schneider  2 , Paul A Bradley  5 , Ira R Katz  6 , Caitlin Thompson  7   8 , Robert M Bossarte  9   10
Affiliations

Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration

Ronald C Kessler et al. Int J Methods Psychiatr Res. 2017 Sep.

Abstract

Objectives: The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here.

Methods: A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009-2011 who used VHA services the year of their death or prior year and a 1% probability sample of time-matched VHA service users alive at the index date (n = 2,112,008).

Results: A penalized logistic model with 61 predictors had sensitivity comparable to the proof-of-concept model (which had 381 predictors) at target thresholds. The machine learning algorithms had relatively similar sensitivities, the highest being for Bayesian additive regression trees, with 10.7% of suicides occurred among the 1.0% of Veterans with highest predicted risk and 28.1% among the 5.0% of with highest predicted risk.

Conclusions: Based on these results, VHA is using penalized logistic regression in initial intervention implementation. The paper concludes with a discussion of other practical issues that might be explored to increase model performance.

Keywords: assessment/diagnosis; clinical decision support; epidemiology; machine learning; predictive modeling; suicide/self harm.

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