Citation

Bender EM, Machetanz L, von Känel R, Euler S, Kirchebner J, Günther MP. Front. Psychiatry 2024; 15: e1356843.

Copyright

(Copyright © 2024, Frontiers Media)

DOI

10.3389/fpsyt.2024.1356843

PMID

38516261

PMCID

PMC10954830

Abstract

INTRODUCTION: Comorbid substance use disorder (SUD) is linked to a higher risk of violence in patients with schizophrenia spectrum disorder (SSD). The objective of this study is to explore the most distinguishing factors between offending and non-offending patients diagnosed with SSD and comorbid SUD using supervised machine learning.

METHODS: A total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms.

RESULTS: Failures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization.

DISCUSSION: This research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.

Language: en

Keywords

explorative analysis; forensic psychiatric patients; non-forensic patients; offending; schizophrenia spectrum disorder; substance use disorder; supervised machine learning