Watts Devon, Mamak Mini, Moulden Heather, Upfold Casey, de Azevedo Cardoso Taiane, Kapczinski Flavio, Chaimowitz Gary
Neuroscience Graduate Program, McMaster University, Hamilton, Canada.
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
J Psychiatr Res. 2023 May;161:91-98. doi: 10.1016/j.jpsychires.2023.02.030. Epub 2023 Mar 1.
The prediction and prevention of aggression in individuals with schizophrenia remains a top priority within forensic psychiatric settings. While risk assessment methods are well rooted in forensic psychiatry, there are no available tools to predict longitudinal physical aggression in patients with schizophrenia within forensic settings at an individual level. In the present study, we used evidence-based risk and protective factors, as well as variables related to course of treatment assessed at baseline, to predict prospective incidents of physical aggression (4-month, 12-month, and 18-month follow-up) among 151 patients with schizophrenia within the forensic mental healthcare system. Across our HARM models, the balanced accuracy (sensitivity + specificity/2) of predicting physical aggressive incidents in patients with schizophrenia ranged from 59.73 to 87.33% at 4-month follow-up, 68.31-80.10% at 12-month follow-up, and 46.22-81.63% at 18-month follow-up, respectively. Additionally, we developed separate models, using clinician rated clinical judgement of short term and immediate violent risk, as a measure of comparison. Several modifiable evidence-based predictors of prospective physical aggression in schizophrenia were identified, including impulse control, substance abuse, impulsivity, treatment non-adherence, mood and psychotic symptoms, substance abuse, and poor family support. To the best of our knowledge, our HARM models are the first to predict longitudinal physical aggression at an individual level in patients with schizophrenia in forensic settings. However, it is important to caution that since these machine learning models were developed in the context of forensic settings, they may not be generalisable to individuals with schizophrenia more broadly. Moreover, a low base rate of physical aggression was observed in the testing set (6.0-11.6% across timepoints). As such, larger cohorts will be required to determine the replicability of these findings.
在法医精神病学环境中，预测和预防精神分裂症患者的攻击行为仍然是首要任务。虽然风险评估方法在法医精神病学中已根深蒂固，但在法医环境中，尚无个体层面预测精神分裂症患者纵向身体攻击行为的可用工具。在本研究中，我们使用基于证据的风险和保护因素，以及基线时评估的与治疗过程相关的变量，来预测法医精神卫生系统中151名精神分裂症患者的前瞻性身体攻击事件（4个月、12个月和18个月随访）。在我们所有的HARM模型中，预测精神分裂症患者身体攻击事件的平衡准确率（敏感性 + 特异性/2）在4个月随访时为59.73%至87.33%，12个月随访时为68.31%至80.10%，18个月随访时为46.22%至81.63%。此外，我们开发了单独的模型，使用临床医生对短期和即时暴力风险的评级临床判断作为比较指标。确定了精神分裂症前瞻性身体攻击行为的几个基于证据的可改变预测因素，包括冲动控制、药物滥用、冲动性、治疗不依从、情绪和精神病症状、药物滥用以及家庭支持不足。据我们所知，我们的HARM模型是首个在法医环境中个体层面预测精神分裂症患者纵向身体攻击行为的模型。然而，必须谨慎的是，由于这些机器学习模型是在法医环境中开发的，它们可能无法更广泛地推广到精神分裂症患者。此外，在测试集中观察到身体攻击行为的低基础发生率（各时间点为6.0 - 11.6%）。因此，将需要更大的队列来确定这些发现的可重复性。
