Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods.

ABSTRACT:

Background

Limited access to drug-susceptibility tests (DSTs) and delays in receiving DST results are challenges for timely and appropriate treatment of multi-drug resistant tuberculosis (TB) in many low-resource settings. We investigated whether data collected as part of routine, national TB surveillance could be used to develop predictive models to identify additional resistance to fluoroquinolones (FLQs), a critical second-line class of anti-TB agents, at the time of diagnosis with rifampin-resistant TB.

Methods and findings

We assessed three machine learning-based models (logistic regression, neural network, and random forest) using information from 540 patients with rifampicin-resistant TB, diagnosed using Xpert MTB/RIF and notified in the Republic of Moldova between Ja

SUBMITTER: You S 

PROVIDER: S-EPMC9518704 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature