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Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study

Published online by Cambridge University Press:  04 June 2020

Diego Librenza-Garcia Open the ORCID record for Diego Librenza-Garcia [Opens in a new window] ,
Ives Cavalcante Passos Open the ORCID record for Ives Cavalcante Passos [Opens in a new window] ,
Jacson Gabriel Feiten Open the ORCID record for Jacson Gabriel Feiten [Opens in a new window] ,
Paulo A. Lotufo ,
Alessandra C. Goulart ,
Itamar de Souza Santos ,
Maria Carmen Viana ,
Isabela M. Benseñor  and
Andre Russowsky Brunoni
Diego Librenza-Garcia
Affiliation:
Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
Ives Cavalcante Passos
Affiliation:
Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Jacson Gabriel Feiten
Affiliation:
Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Paulo A. Lotufo
Affiliation:
Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
Alessandra C. Goulart
Affiliation:
Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
Itamar de Souza Santos
Affiliation:
Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
Maria Carmen Viana
Affiliation:
Department of Social Medicine, Postgraduate Program in Public Health, Center of Psychiatric Epidemiology (CEPEP), Federal University of Espírito Santo, Vitória, Brazil
Isabela M. Benseñor
Affiliation:
Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil
Andre Russowsky Brunoni*
Affiliation:
Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
*
Author for correspondence: Andre Russowsky Brunoni, E-mail: [email protected]
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Abstract

Abstract

Background

Depression is highly prevalent and marked by a chronic and recurrent course. Despite being a major cause of disability worldwide, little is known regarding the determinants of its heterogeneous course. Machine learning techniques present an opportunity to develop tools to predict diagnosis and prognosis at an individual level.

Methods

We examined baseline (2008–2010) and follow-up (2012–2014) data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a large occupational cohort study. We implemented an elastic net regularization analysis with a 10-fold cross-validation procedure using socioeconomic and clinical factors as predictors to distinguish at follow-up: (1) depressed from non-depressed participants, (2) participants with incident depression from those who did not develop depression, and (3) participants with chronic (persistent or recurrent) depression from those without depression.

Results

We assessed 15 105 and 13 922 participants at waves 1 and 2, respectively. The elastic net regularization model distinguished outcome levels in the test dataset with an area under the curve of 0.79 (95% CI 0.76–0.82), 0.71 (95% CI 0.66–0.77), 0.90 (95% CI 0.86–0.95) for analyses 1, 2, and 3, respectively.

Conclusions

Diagnosis and prognosis related to depression can be predicted at an individual subject level by integrating low-cost variables, such as demographic and clinical data. Future studies should assess longer follow-up periods and combine biological predictors, such as genetics and blood biomarkers, to build more accurate tools to predict depression course.

Keywords

Incident depressionmachine learningmajor depressive disorderprognosis
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 16 , December 2021 , pp. 2895 - 2903
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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