Prediction models for suicide attempts and deaths: A systematic review and simulation
Belsher, B.E., Smolenski, D.J., Pruitt, L.D., Bush, N.E., Beech, E.H., Workman, D.E., ... Skopp, N.A.
Importance Suicide prediction models have the potential to improve the identification of patients at heightened suicide risk by using predictive algorithms on large-scale data sources. Suicide prediction models are being developed for use across enterprise-level health care systems including the US Department of Defense, US Department of Veterans Affairs, and Kaiser Permanente.
Objectives To evaluate the diagnostic accuracy of suicide prediction models in predicting suicide and suicide attempts and to simulate the effects of implementing suicide prediction models using population-level estimates of suicide rates.
Evidence Review A systematic literature search was conducted in MEDLINE, PsycINFO, Embase, and the Cochrane Library to identify research evaluating the predictive accuracy of suicide prediction models in identifying patients at high risk for a suicide attempt or death by suicide. Each database was searched from inception to August 21, 2018. The search strategy included search terms for suicidal behavior, risk prediction, and predictive modeling. Reference lists of included studies were also screened. Two reviewers independently screened and evaluated eligible studies.
Findings From a total of 7306 abstracts reviewed, 17 cohort studies met the inclusion criteria, representing 64 unique prediction models across 5 countries with more than 14 million participants. The research quality of the included studies was generally high. Global classification accuracy was good (≥0.80 in most models), while the predictive validity associated with a positive result for suicide mortality was extremely low (≤0.01 in most models). Simulations of the results suggest very low positive predictive values across a variety of population assessment characteristics.
Conclusions and Relevance To date, suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near 0. Several critical concerns remain unaddressed, precluding their readiness for clinical applications across health systems.