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Who is most at risk of dying if infected with SARS-CoV-2? A mortality risk factor analysis using machine learning of patients with COVID-19 over time: a large population-based cohort study in Mexico.
Liao, Lauren D; Hubbard, Alan E; Gutierrez, Juan Pablo; Juárez-Flores, Arturo; Kikkawa, Kendall; Gupta, Ronit; Yarmolich, Yana; de Jesús Ascencio-Montiel, Iván; Bertozzi, Stefano M.
Afiliação
  • Liao LD; Division of Biostatistics, University of California Berkeley, Berkeley, California, USA ldliao@berkeley.edu.
  • Hubbard AE; Division of Biostatistics, University of California Berkeley, Berkeley, California, USA.
  • Gutierrez JP; Center for Policy, Population and Health Research, School of Medicine, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.
  • Juárez-Flores A; Center for Policy, Population and Health Research, School of Medicine, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.
  • Kikkawa K; Micron Technology, Boise, Idaho, USA.
  • Gupta R; College of Computing, Data Science, and Society, University of California Berkeley, Berkeley, California, USA.
  • Yarmolich Y; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, USA.
  • de Jesús Ascencio-Montiel I; Instituto Mexicano del Seguro Social, Ciudad de México, Mexico.
  • Bertozzi SM; Division of Health Policy and Management, University of California, Berkeley, Berkeley, California, USA.
BMJ Open ; 13(9): e072436, 2023 09 22.
Article em En | MEDLINE | ID: mdl-37739469
OBJECTIVE: COVID-19 would kill fewer people if health programmes can predict who is at higher risk of mortality because resources can be targeted to protect those people from infection. We predict mortality in a very large population in Mexico with machine learning using demographic variables and pre-existing conditions. DESIGN: Cohort study. SETTING: March 2020 to November 2021 in Mexico, nationally represented. PARTICIPANTS: 1.4 million laboratory-confirmed patients with COVID-19 in Mexico at or over 20 years of age. PRIMARY AND SECONDARY OUTCOME MEASURES: Analysis is performed on data from March 2020 to November 2021 and over three phases: (1) from March to October in 2020, (2) from November 2020 to March 2021 and (3) from April to November 2021. We predict mortality using an ensemble machine learning method, super learner, and independently estimate the adjusted mortality relative risk of each pre-existing condition using targeted maximum likelihood estimation. RESULTS: Super learner fit has a high predictive performance (C-statistic: 0.907), where age is the most predictive factor for mortality. After adjusting for demographic factors, renal disease, hypertension, diabetes and obesity are the most impactful pre-existing conditions. Phase analysis shows that the adjusted mortality risk decreased over time while relative risk increased for each pre-existing condition. CONCLUSIONS: While age is the most important predictor of mortality, younger individuals with hypertension, diabetes and obesity are at comparable mortality risk as individuals who are 20 years older without any of the three conditions. Our model can be continuously updated to identify individuals who should most be protected against infection as the pandemic evolves.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 / Hipertensão Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans País/Região como assunto: Mexico Idioma: En Revista: BMJ Open Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 / Hipertensão Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans País/Região como assunto: Mexico Idioma: En Revista: BMJ Open Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido