Philipse Martien W G, Koeter Maarten W J, van der Staak Cees P F, van den Brink Wim
Department of Research and Assessment, Pompestichting Institute for Forensic Mental Health, Nij-megen, The Netherlands.
Law Hum Behav. 2006 Jun;30(3):309-27. doi: 10.1007/s10979-006-9013-4.
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.
如果法医精神病学领域的临床医生想要降低患者再次犯罪的风险，他们需要深入了解动态风险因素，以及这些因素能为静态风险指标增添预测力的证据。预测因素需要在临床实际情况下进行评估。本研究旨在通过一项自然主义结局研究验证动态和静态变量作为再次定罪预测因素的有效性。收集了151名从荷兰法医精神病医院出院患者的静态和动态风险因素数据。社区随访是前瞻性的，最短为期5.5年。使用Cox回归分析建立了一个预测模型。该模型预测力的大小通过受试者工作特征（ROC）分析进行估计。最终的预测模型包含四个静态预测因素，没有动态预测因素。该模型的曲线下ROC面积为0.79（95%置信区间0.69 - 0.89）。临床风险评级没有预测性。探索患者亚组影响的事后分析没有得出更好的模型。研究得出结论，一小部分静态预测因素能够很好地估计未来再次定罪的情况；纳入动态预测因素并没有增加预测力。
