OBJECTIVE: To determine predictive factors for antimicrobial resistance patterns and to develop an antimicrobial treatment algorithm for afebrile outpatients presenting with complicated cystitis. MATERIALS AND METHODS: We performed a retrospective, single-center, cross-sectional study of 2,891 outpatients with a diagnosed afebrile complicated cystitis from 2012 to 2018. For patients with confirmed urinary tract infection and antimicrobial sensitivities, univariate analyses and multivariable regression models were used to determine odds ratios for predicting resistance to trimethoprim-sulfamethoxazole, ciprofloxacin, nitrofurantoin, first-generation cephalosporin, and third-generation cephalosporin for the 2012-2016 data. Antimicrobial choice algorithms were created using 2012-2016 results and tested on 2017-2018 data. RESULTS: For afebrile outpatients presenting with complicated cystitis, overall prevalence of resistance for trimethoprim-sulfamethoxazole, ciprofloxacin, nitrofurantoin, first-generation cephalosporin, and third-generation cephalosporin was 25.6%, 19.5%, 19.1%, 15.0%, and 6.9%, respectively. Consistent predictive factors influencing resistance to all 5 antimicrobials were patient place of residence (ZIP code), status of host urinary tract (complicated vs uncomplicated), and prior resistance to the antimicrobial. Resulting treatment algorithm for complicated cystitis (whether or not prior microbiologic data was available) outperformed real-life provider choice and our previously published algorithm for uncomplicated cystitis. CONCLUSION: Treatment algorithms for urinary tract infections are dependent on patient place of residence (ZIP code), status of the host urinary tract (complicated or uncomplicated), and prior urine culture resistance data. When using our complicated cystitis treatment algorithm regardless of uropathogen, our results outperformed real-life scenario provider choice and our prior published algorithm for uncomplicated cystitis, which can help guide empiric antimicrobial choice.
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