[Application of MOAS for Evaluating of Violence Risk in the Inpatients with Mental Disorders]

Empathy deficit in male patients with schizophrenia and its relationships with impulsivity and premeditated violence

Objective

To explore the pattern of empathy characteristics in male patients with schizophrenia (SCH) and to examine whether empathy deficit is associated with impulsivity and premeditated violence.

Methods

One hundred and fourteen male SCH patients were enrolled in this study. The demographic data of all patients were collected and the subjects were divided into two groups, namely, the violent group, including 60 cases, and the non-violent group, comprising 54 cases, according to the Modified Overt Aggression Scale (MOAS). The Chinese version of the Interpersonal Reactivity Index-C (IRI-C) was used to evaluate empathy and the Impulsive/Predicted Aggression Scales (IPAS) was employed to assess the characteristics of aggression.

Results

Among the 60 patients in the violent group, 44 patients had impulsive aggression (IA) and 16 patients had premeditated aggression (PM) according to the IPAS scale. In the violent group, the scores of the four subfactors of the IRI-C, i.e., perspective taking (PT), fantasy (FS), personal distress (PD), and empathy concern (EC), were significantly lower than in the non-violent group. Stepwise logistic regression showed that PM was independent influencing factor for violent behaviors in SCH patients. Correlation analysis revealed that EC of affective empathy was positively correlated with PM but not with IA.

Conclusion

SCH patients with violent behavior had more extensive empathy deficits compared with non-violent SCH patients. EC, IA and PM are independent risk factors of violence in SCH patients. Empathy concern is an important index to predict PM in male patients with SCH.

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

Objective

To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.

Methods

The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.

Results

The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877-0.926), 0.901 (95% CI: 0.874-0.923), 0.902 (95% CI: 0.876-0.924), and 0.955 (95% CI: 0.935-0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).

Conclusion

Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.

Substance Abuse Associated with Aggressive/Violent Behaviors in Psychiatric Outpatients and Related Psychotropic Prescription

Psychiatric disorders with substance abuse are considered the leading causes of most violent and aggressive behaviors in the general population. This study was aimed to assess the impact of substance abuse and the therapeutic approaches adopted by psychiatrists in aggressive vs non-aggressive outpatients (n = 400) attending community-based psychiatric services and recruited over a 3-year period. Clinical and therapeutic variables were collected from medical records and the Modified Overt Aggression Scale (MOAS) was used to assess any aggressive/violent behavior. Violent behaviors were significantly higher in alcohol and substance abusers compared to non-abusers (p < 0.01), except for heroin abusers. Mean weighted MOAS score was significantly higher in patients taking antipsychotics (p < 0.005). The administration of Haloperidol, Zuclopenthixol, and Clozapine was more frequent in aggressive than in non-aggressive patients. The most frequently administered drug in these patients was Haloperidol (23.91%), with a higher mean daily dosage in violent vs non-violent patients. Our results confirm the well-established relationship between substance abuse and violent behaviors in psychiatric inpatients also within outpatient community services. Observed rates of most frequently prescribed antipsychotics to aggressive patients did not show any preference for newer generation compounds, with clinicians operating in the community setting likely being in need for further evidence and specific training to support their treatment choice.

Manic episode, aggressive behavior and poor insight are significantly associated with involuntary admission in patients with bipolar disorders

Objectives

Serious mental illnesses, such as bipolar disorders and schizophrenia, are closely associated with involuntary admission. Many studies have focused on involuntary admission in people with schizophrenia, but little is known about the factors associated with involuntary admission in Chinese patients with bipolar disorders. This study aimed to investigate socio-demographic and clinical factors associated with involuntary admission in Chinese patients with bipolar disorders.

Methods

In this multi-center cross-sectional survey in China, a total of 155 newly admitted patients with bipolar disorders were consecutively recruited from 16 psychiatric institutions from 15 March to 14 April, 2013. Patients' socio-demographic and clinical data were collected from their medical records. The Modified Overt Aggression Scale and the Insight and Treatment Attitudes Questionnaire were used to measure patients' level of aggression and insight of current psychiatric illness.

Results

The prevalence of involuntary admission was 52% in this sample of Chinese inpatients with bipolar disorders. In multiple logistic regression, a high level of aggression (odds ratio (OR) = 2.48), diagnosis of manic episode (OR = 3.65), poor insight (OR = 7.52), and a low level of education (OR = 3.13) were significantly associated with involuntary admission.

Conclusion

Manic episode, aggressive behavior, and poor insight were the significant contributing factors to involuntary admission in Chinese patients with bipolar disorders.