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

Autor: Verboven L; Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerpen, Belgium.; Department of Computer Science, ADReM Data Lab, University of Antwerp, Antwerpen, Belgium., Callens S; Department of Internal Medicine & Infectious diseases, Ghent University Hospital, Ghent, Belgium., Black J; Department of Internal Medicine, University of Cape Town and Livingstone Hospital, Port Elizabeth, South Africa., Maartens G; Department of Medicine, Division of Clinical Pharmacology, University of Cape Town, Cape Town, South Africa., Dooley KE; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America., Potgieter S; Department of Internal Medicine, Division of Infectious Diseases, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa., Cartuyvels R; Department of Computer Science, KU Leuven, Belgium., Laukens K; Department of Computer Science, ADReM Data Lab, University of Antwerp, Antwerpen, Belgium., Warren RM; 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., Van Rie A; Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerpen, Belgium.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Sep 06; Vol. 19 (9), pp. e0306101. Date of Electronic Publication: 2024 Sep 06 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0306101
Abstrakt: 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.
Methods: We developed a treatment recommender clinical decision support system (CDSS) and an accompanying web application for user-friendly recommendation of the optimal individualized treatment regimen to a clinician.
Results: Following expert stakeholder meetings and literature review, nine drug features and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part of a clinical trial and in a routine care setting. Within the clinical trial setting, all patients received the CDSS recommended treatment. In 8 of 20 cases, the initial recommendation was recomputed because of stock out, clinical contra-indication or toxicity. In routine care setting, physicians rejected the treatment recommendation in 7 out of 15 cases because it deviated from the national TB treatment guidelines. A survey indicated that the treatment recommender CDSS is easy to use and useful in clinical practice but requires digital infrastructure support and training.
Conclusions: Our findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of patients with RR-TB, especially those with 'difficult-to-treat' forms of RR-TB.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Verboven et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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