Prevalence of Aggressive or Violent Behaviour in Thai Patients with Schizophrenia: a Cross-Sectional Study

Maneeton, Narong; Maneeton, Benchalak; Jaiyen, Natthanan; Woottiluk, Pakapan; Khemawichanurat, Wajana

doi:10.12809/eaap1775

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Schizophrenia (SCZ) is a debilitating psychiatric disorder, with the prevalence being 1% [ 1 ]. Patients with SCZ have been reported to have a heightened risk of exhibiting violent behavior [ 2 , 3 ]. In comparison with the general population, individuals with SCZ are four to six times more likely to commit violent behavior [ 4 ], which can lead to serious harm to others, higher health care costs, and increased stigmatization associated with this disease [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms

Pei

et al. 2022

BMC Psychiatry

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Background Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention. Methods A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients. Results After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively. Conclusions Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.

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Section: Introductionmentioning

confidence: 99%

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms

Pei

et al. 2022

BMC Psychiatry

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“…Patients with schizophrenia are more likely to exhibit violent behavior, compared with the general population. The relationship of schizophrenia and violent behavior has been repeatedly reported (1)(2)(3). Violent behaviors committed by patients with schizophrenia, not only cause physical damage or death to people, but also increase the social burden (4).…”

Section: Introductionmentioning

confidence: 99%

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Zhang

Liu

et al. 2022

Front. Psychiatry

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BackgroundEarly to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.MethodWe enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.ResultOur results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378–0.816)], having cigarette smoking [2.121 (1.191–3.779)], higher positive syndrome [1.016 (1.002–1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026–1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599–0.7748) had better prediction ability than that of other algorithms.ConclusionML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

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“…Although the sex ratio of criminal acts between women and men has been described as high as 1:10, 101 violent and nonviolent patients have also been found to be comparable in terms of sex. 102 …”

Section: Resultsmentioning

confidence: 99%

Long-acting antipsychotic treatments: focus on women with schizophrenia

Brissos,

Balanzá-Martínez

2024

Therapeutic Advances in Psychopharmacology

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Effective management of schizophrenia (SZ) requires long-term treatment with antipsychotics (APs) to prevent clinical relapse, attain remission and improve patients’ personal and social functioning, and quality of life. Although APs remain the cornerstone treatment for patients with SZ, despite their potential benefits, long-acting injectable APs (LAI-APs) remain underused, most notably in women with SZ. The efficacy and tolerability of APs differ significantly between men and women, and some of these differences are more noticeable depending on the patient’s age and the stage of the disorder. Although sex differences may influence treatment outcomes in SZ, their pertinence has been insufficiently addressed, especially regarding the use of LAI-APs. Some biological and social experiences, such as pregnancy, lactation, contraception and menopause, are specific to women, but these remain under-researched issues. Implications of this disorder in parenting are also of special pertinence regarding women; therefore, taking sex differences into account when treating SZ patients is now recommended, and improving personalized approaches has been proposed as a priority in the management of psychosis. In this narrative, critical review, we address some aspects specific to sex and their implications for the clinical management of women with SZ, with a special focus on the potential role of LAI-AP treatments.

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Prevalence of Aggressive or Violent Behaviour in Thai Patients with Schizophrenia: a Cross-Sectional Study

Cited by 7 publications

References 23 publications

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Long-acting antipsychotic treatments: focus on women with schizophrenia

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