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ABSTRACT: Background
In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility.Methods
Eligible females aged ≥12 years with Escherichia coli-caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015-29 February 2020). Four predictive models were developed to predict nonsusceptibility to each antibiotic class and a r
SUBMITTER: Shields RK
PROVIDER: S-EPMC11327794 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature