Jin Hui, Wang Cong, Yang Yi-Yue, Zhou Lie, Xiao Yun, Wen Yang, Ahmad Jawad, Mu Yun-Fei, Cai Jia, Li Ming, Luo Wei, Zhou Xiao-Fei, Luo Jian-Jun, Liu Bo, Chen Eric Yu-Hai, Ran Mao-Sheng
Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Social Psychiatry, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Front Psychiatry. 2025 Aug 14;16:1586009. doi: 10.3389/fpsyt.2025.1586009. eCollection 2025.
Although identifying factors contributing to aggressive behavior in individuals with schizophrenia is crucial for developing targeted prevention strategies and intervention, most studies were cross-sectional or short-term, and did not take into account the factor of urbanicity. This study aimed to develop a predictive model of aggressive behavior in individuals with schizophrenia in rural China.
A total of 205 individuals with schizophrenia who were identified in 1994 and followed up in 2015 were included in the study. Aggressive behavior was assessed using the Modified Overt Aggression Scale (MOAS). The final predictive model was developed by backward stepwise regression. The model's predictive performance was evaluated using the C statistic and calibration curve.
The rate of aggressive behavior in individuals with schizophrenia in rural China was 36.1% during 1994-2015. The final model of aggressive behavior incorporated the following factors: male, lower educational level, unmarried, with delusion, worse social functioning, and with previous treatment. The model demonstrated acceptable discriminative ability, with an AUC of 0.73, sensitivity of 0.82, and specificity of 0.53. The calibration curve indicated a good fit of the model.
The predictive model developed in this study showed good discriminative ability. A clinically practical nomogram was built to assess the risk of aggressive behavior in individuals with schizophrenia in rural China, which may facilitate early detection and intervention of these individuals, particularly in rural areas with limited resources. This approach may be relevant to similar settings internationally.
尽管确定导致精神分裂症患者攻击性行为的因素对于制定有针对性的预防策略和干预措施至关重要，但大多数研究是横断面研究或短期研究，且未考虑城市化因素。本研究旨在建立中国农村精神分裂症患者攻击性行为的预测模型。
本研究纳入了1994年确诊并于2015年进行随访的205例精神分裂症患者。使用改良外显攻击量表（MOAS）评估攻击性行为。通过向后逐步回归建立最终的预测模型。使用C统计量和校准曲线评估模型的预测性能。
1994 - 2015年期间，中国农村精神分裂症患者的攻击性行为发生率为36.1%。攻击性行为的最终模型纳入了以下因素：男性、教育水平较低、未婚、有妄想、社会功能较差以及曾接受过治疗。该模型显示出可接受的判别能力，曲线下面积（AUC）为0.73，敏感性为0.82，特异性为0.53。校准曲线表明模型拟合良好。
本研究建立的预测模型显示出良好的判别能力。构建了一个临床实用的列线图来评估中国农村精神分裂症患者攻击性行为的风险，这可能有助于对这些患者进行早期检测和干预，特别是在资源有限的农村地区。这种方法在国际上类似的环境中可能也适用。
