Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning.

ABSTRACT:

Background

Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.

Methods

We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa.

Results

Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotyp

SUBMITTER: Dixit A 

PROVIDER: S-EPMC10982777 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature