四川精神卫生2024，Vol.37Issue(1)：39-45,7.DOI:10.11886/scjsws20240104002
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
摘要
Abstract
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.关键词
严重精神障碍/危险行为/预测模型/社区Key words
Severe mental disorder/Dangerous behaviors/Predictive model/Community分类
临床医学引用本文复制引用
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,2024,37(1):39-45,7.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)Funded by Chengdu Medical Research Project(number,2020052) （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）
