Abstract
Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (preculture) antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs), which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25±35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/ sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.
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CITATION STYLE
Tchesnokova, V., Avagyan, H., Rechkina, E., Chan, D., Muradova, M., Haile, H. G., … Sokurenko, E. V. (2017). Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection. PLoS ONE, 12(3). https://doi.org/10.1371/journal.pone.0174132
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