Evidence Hub

Global prevalence of fluoroquinolone resistance in Escherichia coli causing urinary tract infections: a systematic review and meta-analysis

Zhou H, Xue T, Xue J

Frontiers in cellular and infection microbiology, 2026

3 citations

Abstract

Background: Fluoroquinolone resistance in Escherichia coli isolated from urinary tract infections (UTIs) is an increasing global concern, with marked variation across regions and specific agents. Estimating resistance prevalence is essential to guide empirical therapy and strengthen antimicrobial stewardship programs.

Methods: A systematic review and meta-analysis was conducted in accordance with PRISMA guidelines, including English-language observational studies published through December 2025. PubMed, Embase, and Web of Science were searched for reports on fluoroquinolone-resistant E. coli in UTIs. Study quality was evaluated using the modified Newcastle-Ottawa Scale. Pooled prevalence estimates were calculated using a random-effects model, and heterogeneity was assessed with the I² statistic. Subgroup analyses were performed by fluoroquinolone agent. Robustness was examined through sensitivity analyses, while meta-regression explored associations between sample size and resistance prevalence. Publication bias and influential studies were evaluated using DOI and Baujat plots.

Results: Among 9,033 identified records, 36 studies met the inclusion criteria. The overall pooled prevalence of fluoroquinolone resistance in E. coli was 31.09% (95% CI, 24.89%-38.05%). Resistance estimates were 30.32% (95% CI, 22.60%-39.34%) for ciprofloxacin, 27.57% (95% CI, 9.01%-59.40%) for levofloxacin, and 68.75% (95% CI, 55.94%-79.76%) for pefloxacin. Sensitivity analyses demonstrated stable…

How strong is the evidence?

Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free