Algorithms to Enhance Empiric Antimicrobial Choice for Outpatients with Afebrile Complicated Cystitis Reflects Importance of Status of the Urinary Tract and Patient Place of Residence
Abstract
OBJECTIVE
To determine predictive factors for antimicrobial resistance patterns and to develop an antimicrobial treatment algorithm for afebrile outpatients presenting with a 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 2012-2018. For patients with confirmed urinary tract infection (UTI) and antimicrobial sensitivities, univariate analyses and multivariable regression models were used to determine odds ratios for predicting resistance to trimethoprim-sulfamethoxazole (SXT), ciprofloxacin (CIP), nitrofurantoin (NIT), first-generation cephalosporin (CF-1) and third-generation cephalosporin (CF-3) 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 SXT, CIP, NIT, CF-1 and CF-3 was 25.6%, 19.5%, 19.1%, 15.0% and 6.9%, respectively. Consistent predictive factors influencing resistance to all five 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…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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