Risk prediction models of dangerous behaviors among patients with severe mental disorder in community
10.11886/scjsws20240104002
- VernacularTitle:社区严重精神障碍患者危险行为发生风险预测模型
- Author:
Xuanyi HU
1
;
Min XIE
1
;
Siyi LIU
1
;
Yulu WU
1
;
Xiangrui WU
2
;
Yuanyuan LIU
2
;
Changjiu HE
3
;
Guangzhi DAI
3
;
Qiang WANG
1
Author Information
1. Mental Health Center， West China Hospital， Sichuan University， Chengdu 610041， China
2. West China School of Public Health and West China Fourth Hospital， Sichuan University， Chengdu 610041， China
3. The Fourth People's Hospital of Chengdu， Chengdu 610000， China
- Publication Type:Journal Article
- Keywords:
Severe mental disorder;
Dangerous behaviors;
Predictive model;
Community
- From:
Sichuan Mental Health
2024;37(1):39-45
- CountryChina
- Language:Chinese
-
Abstract:
BackgroundThe 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. ObjectiveTo 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. MethodsA 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. ResultsDuring 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%. ConclusionThe 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）］
