Year: 2024
Type: article
Abstract: OBJECTIVE: To develop, externally validate, and test a series of computer algorithms to accurately predict antibiotic susceptibility test (AST) results at the time of clinical diagnosis, up to 3 days before standard urine culture results become available, with the goal of improving antibiotic stewardship and patient outcomes. PATIENTS AND METHODS: Machine learning algorithms were developed and trained to predict susceptibility or resistance using over 4.7 million discrete AST classifications from urine cultures in a cohort of adult patients from outpatient and inpatient settings from 2012 to 2022. The algorithms were validated on a cohort from a geographically-distant hospital system, ~1931 km (~1200 miles) from the training cohort facilities, from the same time period. Finally, algorithms were clinically validated in a contemporary cohort and compared to the empiric therapy prescribed by clinicians. Appropriateness of the antibiotics selected by clinicians and the algorithm during the clinical validation was compared. RESULTS: Algorithms were accurate during clinical validation (area under the receiver operating characteristic curve [AUC] 0.71-0.94) for all 11 tested antibiotics. The algorithms' accuracy improved as the organism was identified (AUC 0.79-0.97). In external validation in a geographically-distant cohort, the algorithms remained accurate even without additional training on this group (AUC 0.69-0.87). When the algorithms were trained on the antibiogram from the geographically-distant hospital, the accuracy improved (AUC 0.70-0.93). When algorithms' performances were tested against clinicians in a contemporary cohort for the empiric prescription of oral antibiotics, the drug agent suggested by the algorithms more frequently resulted in adequate empiric coverage. CONCLUSIONS: Machine learning algorithms trained on a large dataset are accurate in prediction of urine culture susceptibility vs resistance up to 3 days prior to urine AST availability. Clinical implementation of such an algorithm could improve both clinical care and antimicrobial stewardship.
Source: British Journal of Urology
DOI: 10.1111/bju.16626
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405687571