biostudies-literatureStracy MEuropean Research CouncilWellcome TrustNIGMS NIH HHS889-894https://www.ebi.ac.uk/biostudies/studies/S-EPMC7612469biostudies-literatureUnknown375(6583)Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen's susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient's own microbiota, these resistance-gaining recurrences can be predicted using the patient's past infection history and minimized by machine learning-personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.Science (New York, N.Y.)Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections.PMC7612469204684281891R01 GM081617204684/Z/16/ZWolf TKishony RKatz RAmer YHerzel EYelin IKoren GChodick GStracy MFoxman BShalev VParizade MRimler GKuint JSnitser OfalseMinimizing treatment-induced emergence of antibiotic resistance in bacterial infections.Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen's susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient's own microbiota, these resistance-gaining recurrences can be predicted using the patient's past infection history and minimized by machine learning-personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.2022-01-01T00:00:00Z2022 Feb2025-04-04T02:24:01.364Z2025-04-04T02:24:01.364ZS-EPMC76124693520186210.1126/science.abg9868