Algorithms Using Previous Resistance, Prior Antimicrobial Prescriptions, and Patient Place of Residence Enhance Empirical Therapy for Women With Uncomplicated Urinary Tract Infections

Objective: To evaluate how previous antimicrobial resistance, prior prescription data, and patient place of residence (ZIP code) can guide empirical therapy for uncomplicated urinary tract infections (UTI). Guidelines recommend empirical antimicrobial selection for women with symptoms of uncomplicated UTIs, most commonly trimethoprim-sulfamethoxazole (SXT), nitrofurantoin (NIT), or ciprofloxacin (CIP). Previous antimicrobial resistance and prior prescription data are potential predictors of resistance in subsequent urine cultures for UTIs. Also, there is evidence of geographic clustering of antimicrobial resistance for UTIs. Methods: Retrospective data from women (age ≥18) with an assigned diagnosis of UTI, submitting urine cultures as outpatients (2011-2018), were gathered. Univariate analyses and multivariable regression models were used to determine odds ratios for predicting resistance to SXT, NIT, and CIP on the 2011-2017 data. Antimicrobial choice algorithms were created using 2011-2017 results and tested on 2018 data. Results: In the training cohort, 9455 women had diagnoses of uncomplicated UTIs and positive urine cultures. Prevalence of resistance for SXT, NIT, and CIP was 19.4%, 12.1%, and 10.3%, respectively. A urine culture with previous resistance, prior antimicrobial prescription within 2 years and ZIP code were the strongest predictors of a subsequent resistant culture. An algorithm based on these data had a success rate of 92.2%, compared to provider's choice (87.5%, P <.001) or best theoretical outcomes with guidelines (90.0%, P =.048). Conclusion: Previous resistance, prior prescriptions, and patient ZIP code are predictors of subsequent resistance in patients with uncomplicated UTIs. Algorithms using these data can outperform real-world outcomes and guidelines.