胡萱怡,谢敏,刘思弋,吴雨璐,吴祥瑞,刘元元,何昌九,代光智,王强.社区严重精神障碍患者危险行为发生风险预测模型[J].四川精神卫生杂志,2024,37(1):39-45.Hu Xuanyi,Xie Min,Liu Siyi,Wu Yulu,Wu Xiangrui,Liu Yuanyuan,He Changjiu,Dai Guangzhi,Wang Qiang,Risk prediction models of dangerous behaviors among patients with severe mental disorder in community[J].SICHUAN MENTAL HEALTH,2024,37(1):39-45

社区严重精神障碍患者危险行为发生风险预测模型

Risk prediction models of dangerous behaviors among patients with severe mental disorder in community

投稿时间：2024-01-04

DOI：10.11886/scjsws20240104002

中文关键词: 严重精神障碍危险行为预测模型社区

英文关键词:Severe mental disorder Dangerous behaviors Predictive model Community

基金项目:成都市医学科研项目（项目名称：多阶段多层分类社区严重精神障碍患者暴力风险评估与预测研究，项目编号：2020052）

作者	单位	邮编
胡萱怡	四川大学华西医院心理卫生中心，四川成都 610041 成都市第四人民医院，四川成都 610000	610000
谢敏	四川大学华西医院心理卫生中心，四川成都 610041	610041
刘思弋	四川大学华西医院心理卫生中心，四川成都 610041	610041
吴雨璐	四川大学华西医院心理卫生中心，四川成都 610041	610041
吴祥瑞	四川大学华西公共卫生学院，四川大学华西第四医院，四川成都 610041	610041
刘元元	四川大学华西公共卫生学院，四川大学华西第四医院，四川成都 610041	610041
何昌九	成都市第四人民医院，四川成都 610000	610000
代光智	成都市第四人民医院，四川成都 610000	610000
王强	四川大学华西医院心理卫生中心，四川成都 610041	610041

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中文摘要:

背景严重精神障碍患者危险行为发生率较一般人群更高，我国对社区严重精神障碍患者危险行为发生风险的预测研究尚不多见，尤其缺乏除传统预测方法之外的数据挖掘技术预测模型的研究和比较。目的采用Logistic回归分析及分类决策树构建社区严重精神障碍患者危险行为发生风险的预测模型，检验分类决策树模型是否优于Logistic回归模型。方法于2023年12月，选取2013年—2022年随访记录完整的11 484名社区严重精神障碍在管患者，按8∶2随机分为训练集（n=9 186）与测试集（n=2 298）。在训练集中，分别使用Logistic回归分析和分类决策树建立预测模型，在测试集评价模型的区分度和校准度。结果 1 115例（9.71%）严重精神障碍患者在随访期间出现危险行为。Logistic回归分析结果显示，城市户籍、贫困、有监护人、精神残疾、危险行为史阳性、自知力不全、自知力缺失、有阳性症状是患者发生危险行为的危险因素（OR=1.778、1.459、2.719、1.483、3.890、1.423、2.528、2.124，P均<0.01）；年龄≥60岁、受过教育、医嘱无需用药以及社会功能一般是患者发生危险行为的保护因素（OR=0.594、0.824、0.422、0.719，P<0.05或0.01）。基于测试集的ROC曲线下面积（AUC）=0.729（95% CI：0.692~0.766），准确率为70.97%，灵敏度为59.71%，特异度为72.05%；分类决策树结果显示，危险行为史、阳性症状、社会功能总评分、经济状况、自知力、户籍、残疾情况以及年龄是患者发生危险行为的影响因素，基于测试集的AUC=0.721（95% CI：0.705~0.737），准确率为68.28%，灵敏度为64.46%，特异度为68.60%。结论分类决策树模型较Logistic回归模型对社区严重精神障碍患者危险行为发生风险的预测效果不具有更大优势。

英文摘要:

Background The occurrence rate of dangerous behaviors in patients with severe mental disorders is higher than that of the general population. In China， there is limited research on the prediction of dangerous behaviors in community-dwelling patients with severe mental disorders， particularly in terms of predicting models using data mining techniques other than traditional methods.Objective To explore the influencing factors of dangerous behaviors in community-dwelling patients with severe mental disorders and testing whether the classification decision tree model is superior to the Logistic regression model.Methods A total of 11 484 community-dwelling patients with severe mental disorders who had complete follow-up records from 2013 to 2022 were selected on December 2023. The data were divided into a training set （n=9 186） and a testing set （n=2 298） in an 8∶2 ratio. Logistic regression and classification decision trees were separately used to establish predictive models in the training set. Model discrimination and calibration were evaluated in the testing set.Results During the follow-up period， 1 115 cases （9.71%） exhibited dangerous behaviors. Logistic regression results showed that urban residence， poverty， guardianship， intellectual disability， history of dangerous behaviors， impaired insight and positive symptoms were risk factors for dangerous behaviors （OR=1.778， 1.459， 2.719， 1.483， 3.890， 1.423， 2.528， 2.124， P<0.01）. Being aged ≥60 years， educated， not requiring prescribed medication and having normal social functioning were protective factors for dangerous behaviors （OR=0.594， 0.824， 0.422， 0.719， P<0.05 or 0.01）. The predictive effect in the testing set showed an area under curve （AUC） of 0.729 （95% CI： 0.692~0.766）， accuracy of 70.97%， sensitivity of 59.71%， and specificity of 72.05%. The classification decision tree results showed that past dangerous situations， positive symptoms， overall social functioning score， economic status， insight， household registration， disability status and age were the influencing factors for dangerous behaviors. The predictive effect in the testing set showed an AUC of 0.721 （95% CI： 0.705~0.737）， accuracy of 68.28%， sensitivity of 64.46%， and specificity of 68.60%.Conclusion The classification decision tree does not have a greater advantage over the logistic regression model in predicting the risk of dangerous behaviors in patients with severe mental disorders in the community. ［Funded by Chengdu Medical Research Project （number， 2020052）］

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