Differentiating Between Sexual Offending and Violent Non-sexual Offending in Men With Schizophrenia Spectrum Disorders Using Machine Learning.

Forensic psychiatric populations commonly contain a subset of persons with schizophrenia spectrum disorders (SSD) who have committed sex offenses. A comprehensive delineation of the features that distinguish persons with SSD who have committed sex offenses from persons with SSD who have committed violent non-sex offenses could be relevant to the development of differentiated risk assessment, risk management and treatment approaches. This analysis included the patient records of 296 men with SSD convicted of at least one sex and/or violent offense who were admitted to the Centre for Inpatient Forensic Therapy at the University Hospital of Psychiatry Zurich between 1982 and 2016. Using supervised machine learning, data on 461 variables retrospectively collected from the records were compared with respect to their relative importance in differentiating between men who had committed sex offenses and men who had committed violent non-sex offenses. The final machine learning model was able to differentiate between the two types of offenders with a balanced accuracy of 71.5% (95% CI = [60.7, 82.1]) and an AUC of .80 (95% CI = [.67, .93]). The main distinguishing features included sexual behaviours and interests, psychopathological symptoms and characteristics of the index offense. Results suggest that when assessing and treating persons with SSD who have committed sex offenses, it appears to be relevant to not only address the core symptoms of the disorder, but to also take into account general risk factors for sexual recidivism, such as atypical sexual interests and sexual preoccupation.

Differential associations of emotional and physical domains of the MacNew Heart with changes in 6-min walking test.

AIMS: Cardiac rehabilitation (CR), a key component of secondary prevention in cardiac patients, contributes fundamentally to improved cardiovascular health outcomes. Health-related quality of life (HRQOL) represents a widely employed outcome measure in CR, yet, its predictive properties on exercise capacity change during CR are poorly understood. Aim of this study was to examine the association between baseline HRQOL and its subdomains on improvement of exercise capacity during CR. METHODS: Study participants were 13,717 inpatients of six Swiss CR clinics from 2012 to 2018. We measured HRQOL at admission to CR with the MacNew Heart (MNH) questionnaire and exercise capacity at admission and discharge using the six minutes walking test (6MWT). Following factorial analyses, we performed univariate and multivariate analyses to test the predictive properties of baseline global HRQOL and its domains for improvement in exercise capacity, adjusting for demographic and clinical characteristics. RESULTS: Mean improvement in 6MWT was 114 m (SD = 90), achieved after 17.4 days (SD = 5.5). Lower emotional HRQOL (b = 7.85, p = < .001, 95% CI [- 5.67, 10.03]) and higher physical HRQOL (b = - 5.23, p < .001, 95% CI [- 6.56, - 3.90]) were associated with less improvement in the 6MWT. Global MNH and social HRQOL showed no association with exercise capacity improvement. CONCLUSION: Patients entering CR with low emotional and high physical HRQOL are at risk for a lower gain in exercise capacity during CR. Global MNH alone does not provide a reliable assessment of HRQOL; thus a focus on specific domains of HRQOL is needed.

Schizophrenia and substance use disorder: Characteristics of coexisting issues in a forensic setting.

BACKGROUND AND AIMS: Recent research has identified higher prevalence of offending behavior in patients with comorbid schizophrenia spectrum disorder (SSD) and substance use disorder (SUD) compared to patients with SSD only and to the general population. However, findings on the subgroup of patients with SUD, SSD and offending behavior in forensic psychiatric care are scarce and inconsistent. The present study used machine learning to uncover more detailed characteristics of offender patients in forensic psychiatric care with comorbid SSD and SUD. METHODS: Using machine learning algorithms, 370 offender patients (91.6 % male, mean age of M = 34.1, SD = 10.2) and 558 variables were explored in order to build three models to differentiate between no substance use disorder, cannabis use disorder and any other substance use disorder. To counteract the risk of overfitting, the dataset was split, employing variable filtering, machine learning model building and selection embedded in a nested resampling approach on one subset. The best model was then selected and validated on the second data subset. RESULTS: Distinguishing between SUD vs. no drug use disorder yielded models with an AUC of 70 and 78. Variables assignable to demographics, social disintegration, antisocial behavior and illness were identified as most influential for the distinction. The model comparing cannabis use disorder with other substance use disorders provided no significant differences. CONCLUSIONS: From a clinical perspective, offender patients suffering from schizophrenia spectrum and comorbid substance use disorder seem particularly challenging to treat, but initial differences in psychopathology will dissipate over inpatient treatment. Our data suggest that offender patients may benefit from appropriate treatment that focuses on illicit drug abuse to reduce criminal behavior and improve social integration.

Exploring Similarities and Differences of Non-European Migrants among Forensic Patients with Schizophrenia.

Migrants diagnosed with schizophrenia are overrepresented in forensic-psychiatric clinics. A comprehensive characterization of this offender subgroup remains to be conducted. The present exploratory study aims at closing this research gap. In a sample of 370 inpatients with schizophrenia spectrum disorders who were detained in a Swiss forensic-psychiatric clinic, 653 different variables were analyzed to identify possible differences between native Europeans and non-European migrants. The exploratory data analysis was conducted by means of supervised machine learning. In order to minimize the multiple testing problem, the detected group differences were cross-validated by applying six different machine learning algorithms on the data set. Subsequently, the variables identified as most influential were used for machine learning algorithm building and evaluation. The combination of two childhood-related factors and three therapy-related factors allowed to differentiate native Europeans and non-European migrants with an accuracy of 74.5% and a predictive power of AUC = 0.75 (area under the curve). The AUC could not be enhanced by any of the investigated criminal history factors or psychiatric history factors. Overall, it was found that the migrant subgroup was quite similar to the rest of offender patients with schizophrenia, which may help to reduce the stigmatization of migrants in forensic-psychiatric clinics. Some of the predictor variables identified may serve as starting points for studies aimed at developing crime prevention approaches in the community setting and risk management strategies tailored to subgroups of offenders with schizophrenia.