Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis

2021 ◽  
pp. 0306624X2110621
Author(s):  
Johannes René Kappes ◽  
David Alen Huber ◽  
Johannes Kirchebner ◽  
Martina Sonnweber ◽  
Moritz Philipp Günther ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forensic Psychiatry ◽  
Supervised Machine Learning ◽  
Depression And Anxiety ◽  
Schizophrenia Spectrum Disorders ◽  
Forensic Patients ◽  
Self Injury ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
Forensic Psychiatric

The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%. Results suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

scholarly journals Neurocognition and social cognition in patients with schizophrenia spectrum disorders with and without a history of violence: results of a multinational European study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura Iozzino ◽  
Philip D. Harvey ◽  
Nicola Canessa ◽  
Pawel Gosek ◽  
Janusz Heitzman ◽  
...  
Keyword(s):  
Social Cognition ◽  
Cognitive Skills ◽  
Population Data ◽  
Clinical Population ◽  
Schizophrenia Spectrum Disorders ◽  
Forensic Patients ◽  
Schizophrenia Spectrum ◽  
History Of ◽  
History Of Violence ◽  
Spectrum Disorders

Abstract Objective Neurocognitive impairment has been extensively studied in people with schizophrenia spectrum disorders and seems to be one of the major determinants of functional outcome in this clinical population. Data exploring the link between neuropsychological deficits and the risk of violence in schizophrenia has been more inconsistent. In this study, we analyse the differential predictive potential of neurocognition and social cognition to discriminate patients with schizophrenia spectrum disorders with and without a history of severe violence. Methods Overall, 398 (221 cases and 177 controls) patients were recruited in forensic and general psychiatric settings across five European countries and assessed using a standardized battery. Results Education and processing speed were the strongest discriminators between forensic and non-forensic patients, followed by emotion recognition. In particular, increased accuracy for anger recognition was the most distinctive feature of the forensic group. Conclusions These results may have important clinical implications, suggesting potential enhancements of the assessment and treatment of patients with schizophrenia spectrum disorders with a history of violence, who may benefit from consideration of socio-cognitive skills commonly neglected in ordinary clinical practice.

scholarly journals A multinational case−control study comparing forensic and non-forensic patients with schizophrenia spectrum disorders: the EU-VIORMED project

2021 ◽  
pp. 1-11
Author(s):  
Giovanni de Girolamo ◽  
Laura Iozzino ◽  
Clarissa Ferrari ◽  
Pawel Gosek ◽  
Janusz Heitzman ◽  
...  
Keyword(s):  
Personality Disorder ◽  
Interpersonal Violence ◽  
Case Control Study ◽  
Case Control ◽  
Schizophrenia Spectrum Disorders ◽  
Forensic Patients ◽  
Schizophrenia Spectrum ◽  
History Of ◽  
Spectrum Disorders ◽  
Control Study

Abstract Background The relationship between schizophrenia and violence is complex. The aim of this multicentre case–control study was to examine and compare the characteristics of a group of forensic psychiatric patients with a schizophrenia spectrum disorders and a history of significant interpersonal violence to a group of patients with the same diagnosis but no lifetime history of interpersonal violence. Method Overall, 398 patients (221 forensic and 177 non-forensic patients) were recruited across five European Countries (Italy, Germany, Poland, Austria and the United Kingdom) and assessed using a multidimensional standardised process. Results The most common primary diagnosis in both groups was schizophrenia (76.4%), but forensic patients more often met criteria for a comorbid personality disorder, almost always antisocial personality disorder (49.1 v. 0%). The forensic patients reported lower levels of disability and better social functioning. Forensic patients were more likely to have been exposed to severe violence in childhood. Education was a protective factor against future violence as well as higher levels of disability, lower social functioning and poorer performances in cognitive processing speed tasks, perhaps as proxy markers of the negative syndrome of schizophrenia. Forensic patients were typically already known to services and in treatment at the time of their index offence, but often poorly compliant. Conclusions This study highlights the need for general services to stratify patients under their care for established violence risk factors, to monitor patients for poor compliance and to intervene promptly in order to prevent severe violent incidents in the most clinically vulnerable.

Stimulants for impulsive violence in schizophrenia spectrum disordered women: a case series and brief review

CNS Spectrums ◽  
2016 ◽  
Vol 21 (6) ◽  
pp. 445-449 ◽  
Author(s):  
Philip Skoretz ◽  
Chin Tang
Keyword(s):  
Violent Behavior ◽  
Case Series ◽  
Schizophrenia Spectrum Disorders ◽  
Common Concern ◽  
Adequate Control ◽  
Select Group ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
Forensic Psychiatric ◽  
Violence Prevalence

High violence prevalence is a common concern for forensic psychiatric settings. Categorizing underlying drivers of violence has helped to direct treatment and management efforts toward psychotic, predatory, and impulsively violent psychopathology. This article describes a series of cases in which clozapine provided adequate control of psychosis in women suffering schizophrenia-spectrum disorders. Nevertheless, impulsive violence remained problematic. Add-on methylphenidate was found to be safe and effective in curbing impulsive violent behavior in this select group of patients.

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

2020 ◽  
Vol 17 (21) ◽  
pp. 7922
Author(s):  
David A. Huber ◽  
Steffen Lau ◽  
Martina Sonnweber ◽  
Moritz P. Günther ◽  
Johannes Kirchebner
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Criminal History ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Psychiatric Clinic ◽  
Schizophrenia Spectrum Disorders ◽  
Related Factors ◽  
Data Set ◽  
Forensic Psychiatric

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.

scholarly journals Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

2022 ◽  
Vol 12 (2) ◽  
pp. 819
Author(s):  
Lena A. Hofmann ◽  
Steffen Lau ◽  
Johannes Kirchebner
Keyword(s):  
Machine Learning ◽  
Aggressive Behavior ◽  
Antisocial Behavior ◽  
Psychiatric Research ◽  
Support Vector ◽  
Schizophrenia Spectrum Disorders ◽  
Predictive Variables ◽  
Schizophrenia Spectrum ◽  
Vector Machines ◽  
Forensic Psychiatric

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

scholarly journals Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

10.2196/19348 ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. e19348
Author(s):  
Michael Leo Birnbaum ◽  
Prathamesh "Param" Kulkarni ◽  
Anna Van Meter ◽  
Victor Chen ◽  
Asra F Rizvi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Digital Data ◽  
Diagnostic Process ◽  
Schizophrenia Spectrum Disorders ◽  
Internet Search ◽  
Online Search ◽  
Search Activity ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Background Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

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