Citation

Kirchebner J, Günther MP, Sonnweber M, King A, Lau S. BMC Psychiatry 2020; 20(1): e201.

Affiliation

University Hospital of Psychiatry Zurich, Department of Forensic Psychiatry, Zurich, Switzerland.

Copyright

(Copyright © 2020, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12888-020-02612-1

PMID

32375740

PMCID

PMC7201968

Abstract

BACKGROUND: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables.

METHODS: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients' characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay.

RESULTS: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic.

CONCLUSIONS: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically.

RESULTS emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

Language: en

Keywords

Forensic psychiatry; Length of stay; Machine learning; Patient characteristics; Schizophrenic offenders