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

ABSTRACT: 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 antibio

SUBMITTER: Kanjilal S 

PROVIDER: S-EPMC9527766 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature