Heinz, Judith, Röver, Christian, Furaijat, Ghefar, Kaußner, Yvonne, Hummers-Pradier, Eva, Debray, Thomas, Hay, Alastair, Heytens, Stefan, Vik, Ingvild, Little, Paul, Moore, Michael, Stuart, Beth, Wagenlehner, Florian, Kronenberg, Philipp Andreas, Ferry, Sven, Monsen, Tor, Lindbæk, Morten, Friede, Tim and Gágyor, Ildikó (2020) Strategies to reduce antibiotic use in women with uncomplicated urinary tract infection in primary care: protocol of a systematic review and meta-analysis including individual patient data. BMJ Open, 10 (10), e035883. (doi:10.1136/bmjopen-2019-035883).
Abstract
Introduction: uncomplicated urinary tract infection (UTI) in women is a common reason to present in general practice and is usually treated with antibiotics to reduce symptom severity and duration. Results of recent clinical trials indicate that non-antibiotic treatment approaches can also be effective. However, it remains unclear which patients would benefit from antibiotic treatment and which can effectively and safely be treated without antibiotics. This systematic review and meta-analysis aims to estimate the effect of treatment strategies to reduce antibiotic use in comparison with immediate antibiotic treatment and to identify prognostic factors and moderators of treatment effects. A further aim is to identify subgroups of patients benefiting from a specific therapy.
Methods and analysis: a systematic literature search will be performed to identify randomised controlled trials which investigated the effect of treatment strategies to reduce antibiotic use in female adults with uncomplicated UTI compared with immediate antibiotic treatment. Therefore, the primary outcome of the meta-analysis is incomplete recovery. Anonymised individual patient data (IPD) will be collected. Aggregate data will be used for pairwise comparisons of treatment strategies using meta-analysis models with random effects accounting for potential between-study heterogeneity. Potential effect moderators will be explored in meta-regressions. For IPD, generalised linear mixed models will be used, which may be adjusted for baseline characteristics. Interactions of baseline variables with treatment effects will be explored. These models will be used to assess direct comparisons of treatment, but might be extended to networks.
Ethics and dissemination: the local institutional review and ethics board judged the project a secondary analysis of existing anonymous data which meet the criteria for waiver of ethics review. Dissemination of the results will be via published scientific papers and presentations. Key messages will be promoted for example, via social media or press releases.
PROSPERO registration number CRD42019125804.
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