Individuals with severe mental illness are at a higher risk of violence than the general population. However, there is a lack of available and simple tools to screen for the risk of violent offending in clinical settings. We aimed to develop an easy-to-use predictive tool to assist clinicians’ decision-making to identify risk of violent offences in China.
We identified 1157 patients with severe mental illness who committed violent offending and 1304 patients who were not suspected of violent offending in the matched living areas. We used stepwise regression and Lasso’s method to screen for predictors, built a multivariate logistic regression model, and performed internal validation with the 10- fold cross-validation to develop the final prediction model.
The risk prediction model for violence in severe mental illness included age (beta coefficient (b) = 0.05), male sex (b = 2.03), education (b = 1.14), living in rural areas (b = 1.21), history of homeless (b = 0.62), history of previous aggression (b = 1.56), parental history of mental illness (b = 0.69), diagnosis of schizophrenia (b = 1.36), episodes (b = -2.23), duration of illness (b = 0.01). The area under curve for the predictive model for the risk of violence in severe mental illness was 0.93 (95% CI: 0.92-0.94).
In this study, we developed a predictive tool for violent offending in severe mental illness, containing 10 items that can be easily used by healthcare practitioners. The model was internally validated and has the potential for assessing the risk of violence in patients with severe mental illness in community routine care, although external validation is necessary.
Copyright © 2023. Published by Elsevier Ltd.
