Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study

Background Antimicrobial resistance is a major healthcare burden, exasperated when it extends to multiple drugs. While cross-resistance across multiple drugs is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. In addition, bacteria from different sample sources may have undergone different evolutionary trajectories, therefore examining cross-resistance across sources is desirable. 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. ABN modelling allowed for examination of the relationship between resistance to different drugs while controlling for major confounding variables. Findings Patterns of cross-resistance differ across sample sources. Importantly, even when positive cross-resistance exists between two antibiotics in different sample sources, its magnitude may vary substantially. Interpretation Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance and determining antibiotic treatment regimens and policies.