Static and dynamic patient characteristics as predictors of criminal recidivism: A prospective study in a Dutch forensic psychiatric sample.

If clinicians in forensic psychiatry want to reduce risk of reoffending in their patients, they require insight into dynamic risk factors, and evidence that these add predictive power to static risk indicators. Predictors need to be evaluated under clinically realistic circumstances. This study aimed to validate dynamic and static variables as predictors of reconviction in a naturalistic outcome study. Data on static and dynamic risk factors were collected for 151 patients discharged from Dutch forensic psychiatric hospitals. Community follow-up was prospective, with a 5.5 year minimum. A prediction model was developed using Cox regression analysis. The magnitude of the predictive power of this model was estimated using receiver operating characteristic (ROC) analysis. The final prediction model contained four static and no dynamic predictors. The model's ROC area under the curve was .79 (95% CI .69-.89). Clinical risk ratings were non-predictive. Post hoc analyses exploring the influence of subgroups of patients did not yield better models. It is concluded that a small set of static predictors yielded a good estimate of future reconvictions; inclusion of dynamic predictors did not add predictive power.