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. 2024 Sep 6;19(9):e0306101.
doi: 10.1371/journal.pone.0306101. eCollection 2024.

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

Lennert Verboven  1   2 , Steven Callens  3 , John Black  4 , Gary Maartens  5 , Kelly E Dooley  6 , Samantha Potgieter  7 , Ruben Cartuyvels  8 ; SMARTT team; Kris Laukens  2 , Robin M Warren  9 , Annelies Van Rie  1
Affiliations

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

Lennert Verboven et al. PLoS One. .

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.

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Modified technology acceptance model as created by Gagnon et al. [23].
Fig 2
Fig 2. Treatment individualization flow for the first 20 patients in the SMARTT trial WGS arm.
See this image and copyright information in PMC

Update of

References

    1. World Health Organization (WHO), Global Tuberculosis Report 2022. 2022.
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    1. World Health Organization (WHO), Rapid communication: key changes to the treatment of drug-resistant tuberculosis. 2022.
    1. De Vos E., et al., Management of rifampicin-resistant TB: programme indicators and care cascade analysis in South Africa. Int J Tuberc Lung Dis, 2021. 25(2): p. 134–141. doi: 10.5588/ijtld.20.0598 - DOI - PubMed
    1. World Health Organization (WHO), WHO consolidated guidelines on tuberculosis: module 3: diagnosis: rapid diagnostics for tuberculosis detection, 2021 update. 2021.

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