Cargando…

Personal clinical history predicts antibiotic resistance of urinary tract infections

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yelin, Idan, Snitser, Olga, Novich, Gal, Katz, Rachel, Tal, Ofir, Parizade, Miriam, Chodick, Gabriel, Koren, Gideon, Shalev, Varda, Kishony, Roy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962525/
https://www.ncbi.nlm.nih.gov/pubmed/31273328
http://dx.doi.org/10.1038/s41591-019-0503-6
Descripción
Sumario: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 dataset of over 700,000 community-acquired UTIs 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 antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a one-year test period, we find that they much reduce the risk of mismatched treatment compared to the current standard-of-care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.