The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%. Results suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

在法医精神病学住院治疗的罪犯中，自伤的负担是巨大的。这项探索性研究旨在补充先前稀少的关于精神分裂症谱系障碍 (SSD) 住院法医患者自伤相关性的文献。采用在瑞士一家法医精神病医院接受治疗的 356 名 SSD 住院患者作为样本，通过文件分析回顾性收集了 512 个潜在预测变量的患者数据。使用监督机器学习检查数据集，以区分在法医住院期间有自伤行为的患者和没有自残行为的患者。基于十个变量的组合，包括精神病史、犯罪史、精神病理学和药物治疗，最终的机器学习模型能够以 68% 的平衡准确度和 AUC = 71% 的预测能力区分自伤和非自伤。结果表明，自伤的 SSD 法医精神病患者在发病时和首次进入联邦犯罪记录时都更年轻。他们在入院时表现出更严重的精神病理学症状，包括更高水平的抑郁和焦虑以及更难以进行抽象推理。在所有已确定的预测因子中，抑郁和焦虑症状可能是预防住院法医 SSD 患者自伤的最有希望的治疗目标，因为它们具有可修改性，应在未来的研究中进一步证实。