Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment.

Journal: BMC public health
Published Date: May 23, 2024

Abstract

BACKGROUND: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN).

Authors

  • Moreno M S Rodrigues
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil. moreno.rodrigues@fiocruz.br.
  • Beatriz Barreto-Duarte
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
  • Caian L Vinhaes
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
  • Mariana Araújo-Pereira
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
  • Eduardo R Fukutani
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
  • Keityane Bone Bergamaschi
    Laboratório de Análise e Visualização de Dados, Fundação Oswaldo Cruz, Porto Velho, Brazil.
  • Afrânio Kristki
    Programa de Pós-Graduação em Clínica Médica, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
  • Marcelo Cordeiro-Santos
    Fundação Medicina Tropical Doutor Heitor Vieira Dourado, Manaus, Brazil.
  • Valeria C Rolla
    Laboratório de Pesquisa Clínica em Micobacteriose, Instituto Nacional de Infectologia Evandro Chagas, Fiocruz, Rio de Janeiro, Brazil.
  • Timothy R Sterling
    Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
  • Artur T L Queiroz
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil.
  • Bruno B Andrade
    Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil. bruno.andrade@fiocruz.br.

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