Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center.

Journal: BMC psychiatry
PMID: 39048987

Abstract

BACKGROUND: Depression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressive disorder (MDD) and matched controls. We also analyzed subgroups within our MDD sample, including late-life depression and recurrent depression. We further investigated whether machine learning (ML) algorithms could distinguish MDD and each subgroup from controls based on their CoD.

Authors

  • Paula Villela Nunes
    Faculdade de Medicina de Jundiai, rua Francisco Telles, 250, Jundiai, SP, 13202-550, Brazil. paula@formato.com.br.
  • Livia Mancine
    Instituto de Informatica Universidade Federal de Goias, Alameda Palmeiras, s/n, Goiania, GO, 74690-900, Brazil.
  • Beatriz Astolfi Neves
    Faculdade de Medicina de Jundiai, rua Francisco Telles, 250, Jundiai, SP, 13202-550, Brazil.
  • Renata Elaine Paraizo Leite
    Faculdade de Medicina, Universidade de Sao Paulo, Av. Dr. Arnaldo, 455, Sao Paulo, SP, 01246-903, Brazil.
  • Camila Nascimento
    Federal University of Sao Paulo, rua Pedro de Toledo, 669, Sao Paulo, SP, 04039-032, Brazil.
  • Carlos Augusto Pasqualucci
    Faculdade de Medicina, Universidade de Sao Paulo, Av. Dr. Arnaldo, 455, Sao Paulo, SP, 01246-903, Brazil.
  • Beny Lafer
    Bipolar Research Program, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil.
  • Rogerio Salvini
    Instituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brazil.
  • Claudia Kimie Suemoto
    Faculdade de Medicina, Universidade de Sao Paulo, Av. Dr. Arnaldo, 455, Sao Paulo, SP, 01246-903, Brazil.

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