Evaluation of clinical prognostic models for suicide attempts after a major depressive episode

Abstract

In this study, we compare the performance of prognostic models of increasing complexity for prediction of future suicide attempt. Using data from a 2-year prospective study of 304 depressed subjects, a series of Cox proportional hazard regression models were developed to predict future suicide attempt. The models were evaluated in terms of discrimination (the ability to rank subjects in order of risk), calibration (accuracy of predicted probabilities of attempt), and sensitivity and specificity of risk group stratification based on cross-validated predicted probabilities. Although an additive model with past attempt, smoking status, and suicidal ideation achieved 75% (cross-validated) sensitivity and specificity, models that performed best in terms of discrimination included interactions between predictor variables. As several models had similar predictive power, clinical considerations and ease of interpretation may have a significant role in the final stage of model selection for assessing future suicide attempt risk.