Construction and verification of aggressive behavior risk prediction model in stable patients with schizophrenia

Abstract

Background Among all types of mental disorders, individuals with schizophrenia exhibit the highest frequency of aggressive behavior. This disrupts the healthcare environment and poses threats to family life and social harmony. Present approaches fail to identify individuals with schizophrenia who are predisposed to aggressive behavior. In this study, we aimed to construct a risk prediction model for aggressive behavior in stable patients with schizophrenia, which may facilitate early identification of patients who are predisposed to aggression by assessing relevant factors, enabling the management of high-risk groups to mitigate and prevent aggressive behavior. Methods A convenience sample of stable inpatients with schizophrenia were selected from Daqing Municipal Third Hospital and Chifeng Municipal Anding Hospital from March 2021 to July 2023. A total of 429 patients with stable schizophrenia who met the inclusion criteria were included. A survey was conducted with them using a questionnaire consisting of general information questionnaire, Positive and Negative Symptom Scale, Childhood Trauma Questionnaire-Short Form, Connor-Davidson Resilience Scale and Self-esteem Scale. Patients enrolled in this study were divided into aggressive and non-aggressive groups based on whether there was at least one obvious and recorded personal attack episode (including obvious wounding and self-injurious behavior) following diagnosis. Binary Logistic regression was used to determine the influencing factors, and R software was used to establish a nomogram model for predicting the risk of aggressive behavior. Bootstrap method was used for internal validation of the model, and the validation group was used for external validation. C statistic and calibration curve were used to evaluate the prediction performance of the model. Results The model variables included Age, Duration of disease, Positive symptom, Childhood Trauma, Self-esteem and Resilience. The AUROC of the model was 0.790 (95% CI:0.729–0.851), the best cutoff value was 0.308; the sensitivity was 70.0%; the specificity was 81.4%; The C statistics of internal and external validation were 0.759 (95%CI:0.725–0.814) and 0.819 (95%CI:0.733–0.904), respectively; calibration curve and Brier score showed good fit. Conclusions The prediction model has a good degree of discrimination and calibration, which can intuitively and easily screen the high risk of aggressive behavior in stable patients with schizophrenia, and provide references for early screening and intervention.

Effects of cognitive training and behavior modification on aggressive behavior and sleep quality in schizophrenia

“…In patients diagnosed with SCZ, neurocognitive impairments serve as a distant precursor to violent behavior, whereas social cognitive impairments seem to exert a more immediate influence on everyday acts of aggression ( 13 , 16 , 21 ). There is increasing evidence indicating that deficits in both neurocognitive and social cognitive abilities play a role in the regulation of negative emotions and impulsive aggression among individuals diagnosed with SCZ.…”