A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
Abstract
Machine learning models for predicting antibiotic resistance could reduce the use of broad-spectrum antibiotics in urinary tract infection. Triaging antibiotic use Use of broad-spectrum, second-line antibiotics in treating urinary tract infections (UTIs) is increasing, likely due to the prevalence of antibiotic resistance. Kanjilal et al. applied a machine learning approach calibrated to local hospital electronic health record data to predict the probability of resistance to first- and second-line antibiotic therapies for uncomplicated UTI. The algorithm then recommended the least broad-spectrum antibiotic to which a given isolate was predicted nonresistant. Use of the pipeline reduced both broad-spectrum and ineffective antibiotic prescription for UTI in the patient cohort relative to clinicians, suggesting the clinical potential of the approach. Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 3,629 participants or records.
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