OBJECTIVES Patients with schizophrenia have a significant risk of self-harm. We aimed to explore the dynamic relationship between symptomatology, functioning and deliberate self-harm (DSH) and evaluate the feasibility of developing a self-harm risk prediction tool for patients with first-episode schizophrenia (FES). METHODS Patients with FES (n = 1234) were followed up for 36 months. Symptomatology, functioning, treatment adherence and self-harm information were obtained monthly over the follow-up period. A time-varying vector autoregressive (VAR) model was used to study the contribution of clinical variables to self-harm over the 36th month. Random forest models for self-harm were established to classify the individuals with self-harm and predict future self-harm events. RESULTS Over a 36-month period, 187 patients with FES had one or more self-harm events. The depressive symptoms contributed the most to self-harm prediction during the first year, while the importance of positive psychotic symptoms increased from the second year onwards. The random forest model with all static information and symptom instability achieved a good area under the receiver operating characteristic curve (AUROC = 0.77 ± 0.023) for identifying patients with DSH. With a sliding window analysis, the averaged AUROC of predicting a self-event was 0.65 ± 0.102 (ranging from 0.54 to 0.78) with the best model being 6-month predicted future 6-month self-harm for month 11-23 (AUROC = 0.7). CONCLUSIONS Results highlight the importance of the dynamic relationship of depressive and positive psychotic symptoms with self-harm and the possibility of self-harm prediction in FES with longitudinal clinical data.

中文翻译：

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症状的动态模式和预测 3 年以上首发精神分裂症谱系障碍患者故意自伤的功能。

目标 精神分裂症患者有很大的自残风险。我们旨在探索症状学、功能和故意自残 (DSH) 之间的动态关系，并评估为首发精神分裂症 (FES) 患者开发自残风险预测工具的可行性。方法 FES 患者 (n = 1234) 随访 36 个月。在随访期间每月获取症状学、功能、治疗依从性和自我伤害信息。使用时变向量自回归 (VAR) 模型研究临床变量对第 36 个月自残的影响。建立了自残随机森林模型，对自残个体进行分类并预测未来的自残事件。结果 在 36 个月的时间里，187 名 FES 患者有一次或多次自残事件。在第一年，抑郁症状对自我伤害预测的贡献最大，而从第二年开始，积极精神病症状的重要性增加。具有所有静态信息和症状不稳定性的随机森林模型在接受者操作特征曲线下（AUROC = 0.77 ± 0.023）实现了良好的面积，用于识别 DSH 患者。通过滑动窗口分析，预测自我事件的平均 AUROC 为 0.65 ± 0.102（范围从 0.54 到 0.78），最佳模型是 6 个月预测未来 6 个月的第 11-23 个月自我伤害（AUROC = 0.7).