Using microbiological data to improve the use of antibiotics for respiratory tract infections: A protocol for an individual patient data meta-analysis

by Irene Boateng, Beth Stuart, Taeko Becque, Bruce Barrett, Jennifer Bostock, Robin Bruyndonckx, Lucy Carr-Knox, Emily J. Ciccone, Samuel Coenen, Mark Ebell, David Gillespie, Gail Hayward, Katarina Hedin, Kerenza Hood, Tin Man Mandy Lau, Paul Little, Dan Merenstein, Edgar Mulogo, Jose Ordóñez-Mena, Peter Muir, Kirsty Samuel, Nader Shaikh, Sharon Tonner, Alike W. van der Velden, Theo Verheij, Kay Wang, Alastair D. Hay, Nick Francis

Resistance to antibiotics is rising and threatens future antibiotic effectiveness. ‘Antibiotic targeting’ ensures patients who may benefit from antibiotics receive them, while being safely withheld from those who may not. Point-of-care tests may assist with antibiotic targeting by allowing primary care clinicians to establish if symptomatic patients have a viral, bacterial, combined, or no infection. However, because organisms can be harmlessly carried, it is important to know if the presence of the virus/bacteria is related to the illness for which the patient is being assessed. One way to do this is to look for associations with more severe/prolonged symptoms and test results. Previous research to answer this question for acute respiratory tract infections has given conflicting results with studies has not having enough participants to provide statistical confidence.

To undertake a synthesis of IPD from both randomised controlled trials (RCTs) and observational cohort studies of respiratory tract infections (RTI) in order to investigate the prognostic value of microbiological data in addition to, or instead of, clinical symptoms and signs.

A systematic search of Cochrane Central Register of Controlled Trials, Ovid Medline and Ovid Embase will be carried out for studies of acute respiratory infection in primary care settings. The outcomes of interest are duration of disease, severity of disease, repeated consultation with new/worsening illness and complications requiring hospitalisation. Authors of eligible studies will be contacted to provide anonymised individual participant data. The data will be harmonised and aggregated. Multilevel regression analysis will be conducted to determine key outcome measures for different potential pathogens and whether these offer any additional information on prognosis beyond clinical symptoms and signs.

PROSPERO Registration number: CRD42023376769.