Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source.

ABSTRACT:

Background

Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources.

Methods

We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumonia

SUBMITTER: Cherny SS 

PROVIDER: S-EPMC10154291 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature