Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Journal article

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.

Authors: Stracy, M; Snitser, O; Yelin, I; Amer, Y; Parizade, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Association for the Advancement of Science
• Journal: Science
• Volume: 375
• Issue: 6583
• Pages: 889-894
• Publication date: 2022-02-25
• Acceptance date: 2021-12-23
• DOI: 10.1126/science.abg9868
• EISSN: 1095-9203
• ISSN: 0036-8075
• Language: English
• Keywords: FFR