RESUMEN
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）］
RESUMEN
ObjectiveTo explore the related factors of troublemaking behaviors among patients with mental disorders induced by amphetamine-type stimulants （ATS）， and to provide references for the formulation of relevant intervention measures for ATS-induced mental disorders. MethodsA total of 105 patients who met the diagnostic criteria of International Classification of Diseases， tenth edition （ICD-10） for ATS-induced mental disorders were included， and classified into troublemaking group and non-troublemaking group. The general demographic data and clinical data of the selected individuals were collected， and all patients were assessed using Social Support Rating Scale （SSRS）. Then univariate analysis and multivariate Logistic regression model were used to screen the related factors of troublemaking behaviors. ResultsThe scores of SSRS， objective support dimension and social support utilization dimension were significantly lower in troublemaking group than those in non-troublemaking group， with statistical differences ［（24.10±6.59） vs. （28.94±5.59）， t=3.364， P=0.001； （5.50±1.96） vs. （8.20±2.13）， t=5.183， P<0.01； （4.60±2.26） vs. （6.28±1.90）， t=3.435， P=0.001］. Multivariate Logistic regression analysis showed that male （OR=6.061， P=0.014） was a risk factor， while high social support level （OR=0.873， P=0.018） was the protective factor for troublemaking behaviors among patients with ATS-induced mental disorders. ConclusionPatients with ATS-induced mental disorders of the males and with low social support level are at high risk of troublemaking behaviors.