BACKGROUND: The efficacies and adverse effects of different antibiotics for uncomplicated urinary tract infections (UTIs) have been studied by standard meta-analytic methods using pairwise direct comparisons of antimicrobial treatments: the effects of one treatment are compared to those of either another treatment or placebo. However, for clinical decisions, we need to know the effectiveness of each possible treatment in comparison with all relevant alternatives, not with just one. OBJECTIVES: To compare the efficacies and adverse effects of all relevant antibiotics for UTI treatment simultaneously by performing a network meta-analysis using direct and indirect treatment comparisons. METHODS: Using logistic regression analysis, we performed a network meta-analysis of randomized controlled trials (RCTs) published after 1999 that compared different oral antibiotic or placebo regimens for UTI treatment in general practice or outpatient settings. We looked at five binary outcomes: early clinical, early bacteriological, late clinical and late bacteriological outcomes, as well as adverse effects. Consequently, a ranking of the antibiotic regimens could be composed. RESULTS: Using a network structure, we could compare and rank nine treatments from 10 studies. Overall, ciprofloxacin and gatifloxacin appeared the most effective treatments, and amoxicillin-clavulanate appeared the least effective treatment. In terms of adverse effects, there were no significant differences. DISCUSSION: Network meta-analysis shows some clear efficacy differences between different antibiotic treatments for UTI in women. It provides a useful tool for clinical decision making in everyday practice. Moreover, the method can be used for meta-analyses of RCTs across primary care and beyond.
BACKGROUND: The efficacies and adverse effects of different antibiotics for uncomplicated urinary tract infections (UTIs) have been studied by standard meta-analytic methods using pairwise direct comparisons of antimicrobial treatments: the effects of one treatment are compared to those of either another treatment or placebo. However, for clinical decisions, we need to know the effectiveness of each possible treatment in comparison with all relevant alternatives, not with just one. OBJECTIVES: To compare the efficacies and adverse effects of all relevant antibiotics for UTI treatment simultaneously by performing a network meta-analysis using direct and indirect treatment comparisons. METHODS: Using logistic regression analysis, we performed a network meta-analysis of randomized controlled trials (RCTs) published after 1999 that compared different oral antibiotic or placebo regimens for UTI treatment in general practice or outpatient settings. We looked at five binary outcomes: early clinical, early bacteriological, late clinical and late bacteriological outcomes, as well as adverse effects. Consequently, a ranking of the antibiotic regimens could be composed. RESULTS: Using a network structure, we could compare and rank nine treatments from 10 studies. Overall, ciprofloxacin and gatifloxacin appeared the most effective treatments, and amoxicillin-clavulanate appeared the least effective treatment. In terms of adverse effects, there were no significant differences. DISCUSSION: Network meta-analysis shows some clear efficacy differences between different antibiotic treatments for UTI in women. It provides a useful tool for clinical decision making in everyday practice. Moreover, the method can be used for meta-analyses of RCTs across primary care and beyond.
Authors: Sinead Duane; Claire Beecher; Akke Vellinga; Andrew W Murphy; Martin Cormican; Andrew Smyth; Patricia Healy; Michael Moore; Paul Little; Declan Devane Journal: JAC Antimicrob Resist Date: 2022-03-22
Authors: Cara L Grimes; Ethan M Balk; Catrina C Crisp; Danielle D Antosh; Miles Murphy; Gabriela E Halder; Peter C Jeppson; Emily E Weber LeBrun; Sonali Raman; Shunaha Kim-Fine; Cheryl Iglesia; Alexis A Dieter; Ladin Yurteri-Kaplan; Gaelen Adam; Kate V Meriwether Journal: Int Urogynecol J Date: 2020-04-27 Impact factor: 2.894
Authors: Anne M Butler; Michael J Durkin; Matthew R Keller; Yinjiao Ma; William G Powderly; Margaret A Olsen Journal: Clin Infect Dis Date: 2022-04-28 Impact factor: 20.999