Journal Club BJUI 2024 Predictive Models for Antibiotic Susceptibility of Urine Cultures

This research article details the development and validation of machine learning algorithms to predict antibiotic susceptibility in urine cultures. Using a massive dataset of over 4.7 million urine culture results, the researchers created algorithms that accurately predicted susceptibility or resistance to eleven common antibiotics up to three days before standard test results were available. The algorithms were successfully validated in geographically separate hospital systems, demonstrating their generalizability. Importantly, in a clinical trial, the algorithms outperformed clinicians in selecting appropriate empiric antibiotic treatment, suggesting a significant potential for improving patient care and antimicrobial stewardship.
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