Meerwijk Esther L, Finlay Andrea K, Harris Alex H S
VA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
VA National Center on Homelessness Among Veterans, Tampa, FLA, USA.
Npj Ment Health Res. 2025 Jul 10;4(1):29. doi: 10.1038/s44184-025-00143-9.
Although patients with criminal legal system involvement have among the highest rates of suicide, the model that identifies patients at high risk of suicide at the United States Veterans Health Administration (VHA) does not include predictors specific to criminal legal system involvement. We explored whether the model's predictive ability would be improved (1) by retraining the model for legal-involved veterans and (2) by adding additional predictors associated with legal-involvement. For a combined outcome of suicide attempt or suicide death, the retrained models showed a positive predictive value (PPV) of 0.124 and false negative rate (FNR) of 0.527. Adding additional predictors associated with being legal-involved did not improve predictive accuracy. Retraining the VHA suicide risk prediction model for legal-involved patients improves the model's predictive ability for this group of high-risk patients, more so than adding predictors associated with being legal-involved. A similar approach for other high-risk patients is worth exploring.
尽管涉及刑事法律系统的患者自杀率位居前列，但美国退伍军人健康管理局（VHA）用于识别自杀高风险患者的模型并未纳入与刑事法律系统参与相关的预测因素。我们探讨了该模型的预测能力是否会得到提升：（1）通过对涉及法律问题的退伍军人重新训练模型，以及（2）通过添加与法律参与相关的额外预测因素。对于自杀未遂或自杀死亡的综合结果，重新训练后的模型显示阳性预测值（PPV）为0.124，假阴性率（FNR）为0.527。添加与法律参与相关的额外预测因素并未提高预测准确性。对涉及法律问题的患者重新训练VHA自杀风险预测模型，相较于添加与法律参与相关的预测因素，能更好地提升该模型对这组高风险患者的预测能力。对其他高风险患者采用类似方法值得探索。
