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Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis

Published online by Cambridge University Press:  16 July 2020

Neusa Aita Agne Open the ORCID record for Neusa Aita Agne [Opens in a new window] ,
Caroline Gewehr Tisott ,
Pedro Ballester ,
Ives Cavalcante Passos  and
Ygor Arzeno Ferrão
Neusa Aita Agne*
Affiliation:
Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil
Caroline Gewehr Tisott
Affiliation:
Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil
Pedro Ballester
Affiliation:
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
Ives Cavalcante Passos
Affiliation:
Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre (RS), Brazil Department of Psychiatry, Universidade Federal do Rio Grande do Sul, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Porto Alegre, Brazil
Ygor Arzeno Ferrão
Affiliation:
Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre (RS), Brazil Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders (C-TOC), Porto Alegre, Brazil
*
Author for correspondence: Neusa Aita Agne, E-mail: neusaagne@gmail.com
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Abstract

Background

Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear – whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm.

Methods

A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables.

Results

The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95.

Conclusions

This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

Keywords

Algorithmelastic netmachine learningobsessive-compulsive disorderpredictors of suicidesuicide attempts
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 4 , March 2022 , pp. 715 - 725
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Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

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