Research ArticleAntibiotics

A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection

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Science Translational Medicine  04 Nov 2020:
Vol. 12, Issue 568, eaay5067
DOI: 10.1126/scitranslmed.aay5067

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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 susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations.

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