High Risk, High Dose?-Pharmacotherapeutic Prescription Patterns of Offender and Non-Offender Patients with Schizophrenia Spectrum Disorder

Blunted niacin skin flushing response in violent offenders with schizophrenia: A potential auxiliary diagnostic biomarker

Schizophrenia (SZ) is associated with an increased risk of violence, with clinical diagnosis primarily relies on symptomatology. The niacin skin flushing response (NSFR) is proposed as a potential biomarker for SZ, but its effectiveness in violent offenders with schizophrenia (VOSZ) remains unevaluated. This study investigates whether the diagnostic model differentiating general SZ patients (GSZ) from healthy controls (HCs) using NSFR can also distinguish VOSZ from HCs. SZ patients were continuously sampled based on the International Classification of Diseases, 10th Edition, and categorized into VOSZ (with a history of violent crimes), and GSZ (without such history). HCs had no psychiatric illnesses or violent crime history. A total of 315 VOSZ, 296 GSZ, and 281 HCs were recruited. Least absolute shrinkage and selection operator regression was used to select variables and construct diagnostic models based on NSFR. No significant differences in age, sex or BMI were observed among groups. Both VOSZ and GSZ exhibited similar blunted NSFR compared to HCs. The diagnostic model constructed by 14 NSFR variables distinguishing GSZ from HCs was successfully transferred to distinguish VOSZ from HCs, with areas under the curve of 0.796 (specificity = 81.6%, sensitivity = 64.2%) and 0.798 (specificity = 80.0%, sensitivity = 70.2%), respectively. Moreover, NSFR was unrelated to illness severity, violence, or antipsychotic dosage in VOSZ, suggesting it is a trait indicator of SZ. This study supports the NSFR as an objective diagnostic biomarker for distinguishing VOSZ from HCs, expanding its applicability, although it may not specifically identify violent offenders among SZ patients.

Construction of a troublemaking risk assessment tool for patients with severe mental disorders in community of China

OBJECTIVE

Construction a troublemaking risk assessment tool to predict the risk of troublemaking for patients with severe mental disorders in the community of China.

METHODS

28,000 cases registered in the Jiangsu Provincial Severe Mental Disorder Management System from January 2017 to December 2019 were collected. The risk factors of troublemaking among patients with severe mental disorders in the community were analyzed through Logistic regression analysis, then the troublemaking risk assessment tool was established and verified.

RESULTS

The incidence of troublemaking among patients with severe mental disorders in the community was 7.15%. The results of multivariate logistic regression analysis showed that males, ≤ 44 years old, duration of disease ≤ 14 years, high school education and below, unemployed, subsistence allowances, schizophrenia, major symptoms > 1, psychiatric visits ≥ 1 time per year, unwilling to participate in community management and community rehabilitation activities, and delayed diagnosis < 2 months were risk factors for troublemaking. The above factors were incorporated into the nomogram model, and the area under the ROC curve of the nomogram model was 0.688 (95%CI: 0.563-0.726). The calibration curve proved that the probability predicted by the model was in good agreement with the actual probability.

CONCLUSION

The established troublemaking risk assessment tool for patients with severe mental disorders in the community based on Logistic regression analysis had good predictive performance, which could be applied to assess the probability of troublemaking among patients with severe mental disorders in the community.

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review

Background

Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.

Methods

We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.

Results

We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.

Conclusion

ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

Patients with schizophrenia spectrum disorders (SSD) have an elevated risk of suicidality. The same has been found for people within the penitentiary system, suggesting a cumulative effect for offender patients suffering from SSD. While there appear to be overlapping characteristics, there is little research on factors distinguishing between offenders and non-offenders with SSD regarding suicidality. Our study therefore aimed at evaluating distinguishing such factors through the application of supervised machine learning (ML) algorithms on a dataset of 232 offenders and 167 non-offender patients with SSD and history of suicidality. With an AUC of 0.81, Naïve Bayes outperformed all other ML algorithms. The following factors emerged as most powerful in their interplay in distinguishing between offender and non-offender patients with a history of suicidality: Prior outpatient psychiatric treatment, regular intake of antipsychotic medication, global cognitive deficit, a prescription of antidepressants during the referenced hospitalisation and higher levels of anxiety and a lack of spontaneity and flow of conversation measured by an adapted positive and negative syndrome scale (PANSS). Interestingly, neither aggression nor overall psychopathology emerged as distinguishers between the two groups. The present findings contribute to a better understanding of suicidality in offender and non-offender patients with SSD and their differing characteristics.