Personal clinical history predicts antibiotic resistance of urinary tract infections

Idan Yelin, Olga Snitser, Gal Novich, Rachel Katz, Ofir Tal, Miriam Parizade, Gabriel Chodick, Gideon Koren, Varda Shalev, Roy Kishony

Research output: Contribution to journalArticlepeer-review

Abstract

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed ‘empirically’, in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

Original languageEnglish
Pages (from-to)1143-1152
Number of pages10
JournalNature Medicine
Volume25
Issue number7
DOIs
StatePublished - 1 Jul 2019

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