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Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the
Alina Peluso1, Jorge Ramirez Osorio2,3, William H Kazanis4
1Oak Ridge National Laboratory, Oak Ridge, TN, USA pelusoa@ornl.gov Jodie.Trafton@va.gov.
Background:
Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention.
Objective:
REACH VET 1.0 (RV 1.0) was developed on 2008-2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation.
Methods:
RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018-2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016-2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata.
Results:
RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1).
Conclusions:
RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.
Clinical Implications:
RV 2.0 enables targeted interventions and, since its national VHA implementation in June 2025, continues to support system-wide suicide prevention.
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