A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis.

Journal: PloS one
PMID: 39241084

Abstract

BACKGROUND: Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen.

Authors

  • Lennert Verboven
    Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerpen, Belgium.
  • Steven Callens
    Department of Internal Medicine & Infectious diseases, Ghent University Hospital, Ghent, Belgium.
  • John Black
    Department of Internal Medicine, University of Cape Town and Livingstone Hospital, Port Elizabeth, South Africa.
  • Gary Maartens
    Department of Medicine, Division of Clinical Pharmacology, University of Cape Town, Cape Town, South Africa.
  • Kelly E Dooley
    Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Samantha Potgieter
    Department of Internal Medicine, Division of Infectious Diseases, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa.
  • Ruben Cartuyvels
    Department of Computer Science, KU Leuven, Belgium.
  • Kris Laukens
    Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium.
  • Robin M Warren
    DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Stellenbosch University, Cape Town, South Africa.
  • Annelies Van Rie
    Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerpen, Belgium.

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