Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling. Issue 7 (1st July 2021)

• Record Type: Journal Article
• Authors: Venekamp, Roderick P; Hoogland, Jeroen; van Smeden, Maarten; Rovers, Maroeska M; De Sutter, An I; Merenstein, Daniel; van Essen, Gerrit A; Kaiser, Laurent; Liira, Helena; Little, Paul; Bucher, Heiner CC; Reitsma, Johannes B
• Is Part Of: BMJ open. Volume 11:Issue 7(2021)
• Publication Date: 2021-07-01
• Subjects: primary care -- epidemiology -- otolaryngology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-047186
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. Methods and analysis: This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8–15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal–external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. Ethics and dissemination: In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO registration number: CRD42020220108.