Ross EL, Zuromski KL, Reis BY, et al. - As there are several statistical models for predicting suicide risk, researchers herein sought for threshold values of sensitivity, specificity, and positive predictive value that must be attained by a suicide risk prediction method to cost-effectively target a suicide risk reduction intervention to high-risk individuals. In this economic evaluation, published data on suicide epidemiology, the healthcare and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions, were incorporated into a novel decision analytic model. Individuals at high predicted risk were delivered two possible interventions: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual healthcare cost, $1,088). Findings suggest that to be cost-effective for targeting a safety planning and telephone call intervention, suicide risk prediction model should have specificity 95% or higher and sensitivity 17% or higher, corresponding to a positive predictive value of 1% or greater. The necessitated positive predictive value was 2% or greater for a more expensive cognitive behavioral therapy intervention. The accuracy thresholds identified in this analysis were surpassed by several existing suicide risk prediction models.