The HARM models: Predicting longitudinal physical aggression in patients with schizophrenia at an individual level.

Autor: Watts D; Neuroscience Graduate Program, McMaster University, Hamilton, Canada. Electronic address: wattsd21@gmail.com., Mamak M; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Electronic address: mamakm@stjosham.on.ca., Moulden H; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Electronic address: hmoulden@stjosham.on.ca., Upfold C; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Electronic address: cupfold@stjosham.on.ca., de Azevedo Cardoso T; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Electronic address: taianeacardoso@gmail.com., Kapczinski F; Neuroscience Graduate Program, McMaster University, Hamilton, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, Brazil. Electronic address: flavio.kapczinski@gmail.com., Chaimowitz G; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Electronic address: chaimow@mcmaster.ca.
Jazyk: angličtina
Zdroj: Journal of psychiatric research [J Psychiatr Res] 2023 May; Vol. 161, pp. 91-98. Date of Electronic Publication: 2023 Mar 01.
DOI: 10.1016/j.jpsychires.2023.02.030
Abstrakt: The prediction and prevention of aggression in individuals with schizophrenia remains a top priority within forensic psychiatric settings. While risk assessment methods are well rooted in forensic psychiatry, there are no available tools to predict longitudinal physical aggression in patients with schizophrenia within forensic settings at an individual level. In the present study, we used evidence-based risk and protective factors, as well as variables related to course of treatment assessed at baseline, to predict prospective incidents of physical aggression (4-month, 12-month, and 18-month follow-up) among 151 patients with schizophrenia within the forensic mental healthcare system. Across our HARM models, the balanced accuracy (sensitivity + specificity/2) of predicting physical aggressive incidents in patients with schizophrenia ranged from 59.73 to 87.33% at 4-month follow-up, 68.31-80.10% at 12-month follow-up, and 46.22-81.63% at 18-month follow-up, respectively. Additionally, we developed separate models, using clinician rated clinical judgement of short term and immediate violent risk, as a measure of comparison. Several modifiable evidence-based predictors of prospective physical aggression in schizophrenia were identified, including impulse control, substance abuse, impulsivity, treatment non-adherence, mood and psychotic symptoms, substance abuse, and poor family support. To the best of our knowledge, our HARM models are the first to predict longitudinal physical aggression at an individual level in patients with schizophrenia in forensic settings. However, it is important to caution that since these machine learning models were developed in the context of forensic settings, they may not be generalisable to individuals with schizophrenia more broadly. Moreover, a low base rate of physical aggression was observed in the testing set (6.0-11.6% across timepoints). As such, larger cohorts will be required to determine the replicability of these findings.
Competing Interests: Declaration of competing interest Devon Watts reports a CIHR Doctoral Scholarship, outside of the submitted work. Taiane de Azevedo Cardoso reports a CIHR Postdoctoral Scholarship, outside of the submitted work. Heather Moulden, Mini Mamak, Casey Upfold, and Gary Chaimowitz report no biomedical financial interests or potential conflicts of interest. Flávio Kapczinski reports personal fees from Daiichi sankyo, and Janssen-Cilag; grants from Stanley Medical Research Institute 07TGF/1148, grants from INCT - CNPq 465458/2014-9, and from the Canadian Foundation for Innovation - CFI, outside the submitted work.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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