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Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population

Published online by Cambridge University Press:  14 January 2021

Cristiane dos Santos Machado Open the ORCID record for Cristiane dos Santos Machado [Opens in a new window] ,
Pedro L. Ballester ,
Bo Cao ,
Benson Mwangi ,
Marco Antonio Caldieraro ,
Flávio Kapczinski  and
Ives Cavalcante Passos
Cristiane dos Santos Machado
Affiliation:
Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil Department of Psychiatry, Faculty of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Pedro L. Ballester
Affiliation:
Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
Bo Cao
Affiliation:
Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
Benson Mwangi
Affiliation:
Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, USA
Marco Antonio Caldieraro
Affiliation:
Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil Department of Psychiatry, Faculty of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Flávio Kapczinski
Affiliation:
Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil Department of Psychiatry, Faculty of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil Department of Psychiatry and Behavioural Neurosciences, McMaster University and St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
Ives Cavalcante Passos*
Affiliation:
Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil Department of Psychiatry, Faculty of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
*
Author for correspondence: Ives Cavalcante Passos, E-mail: ivescp1@gmail.com
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Abstract

Background

There is still little knowledge of objective suicide risk stratification.

Methods

This study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2.

Results

The model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance.

Conclusions

Risk for suicide attempt can be estimated with high accuracy.

Keywords

Suicidemachine learningpredictionNESARCdepression
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 14 , October 2022 , pp. 2985 - 2996
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Copyright
Copyright © The Author(s) 2021. Published by Cambridge University Press

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