RESUMEN
Our article provides guidance on how to interpret a meta-analysis and introduces the reader to the basics of the underlying statistical analysis. The multiple steps of a meta-analysis including systematic literature search, risk of bias assessment, data extraction and data aggregation are addressed. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach allows to score the quality of the evidence of the results revealed by a meta-analysis. Trial sequential analysis has been suggested in recent years as a method to assess the power of a meta-analysis and the risks of false positive or false negative conclusions. We also provide information on other more complex meta-analytical approaches including network meta-analysis for the comparison of several treatments as well as recent developments such as individual patient data meta-analysis and living meta-analysis.
RESUMEN
INTRODUCTION: Chronic post-surgical pain (CPSP) after lung or pleural surgery is a common complication and associated with a decrease in quality of life, long-term use of pain medication and substantial economic costs. An abundant number of primary prognostic factor studies are published each year, but findings are often inconsistent, methods heterogeneous and the methodological quality questionable. Systematic reviews and meta-analyses are therefore needed to summarise the evidence. METHODS AND ANALYSIS: The reporting of this protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist. We will include retrospective and prospective studies with a follow-up of at least 3 months reporting patient-related factors and surgery-related factors for any adult population. Randomised controlled trials will be included if they report on prognostic factors for CPSP after lung or pleural surgery. We will exclude case series, case reports, literature reviews, studies that do not report results for lung or pleural surgery separately and studies that modified the treatment or prognostic factor based on pain during the observation period. MEDLINE, Scopus, Web of Science, Embase, Cochrane, CINAHL, Google Scholar and relevant literature reviews will be searched. Independent pairs of two reviewers will assess studies in two stages based on the PICOTS criteria. We will use the Quality in Prognostic Studies tool for the quality assessment and the CHARMS-PF checklist for the data extraction of the included studies. The analyses will all be conducted separately for each identified prognostic factor. We will analyse adjusted and unadjusted estimated measures separately. When possible, evidence will be summarised with a meta-analysis and otherwise narratively. We will quantify heterogeneity by calculating the Q and I2 statistics. The heterogeneity will be further explored with meta-regression and subgroup analyses based on clinical knowledge. The quality of the evidence obtained will be evaluated according to the Grades of Recommendation Assessment, Development and Evaluation guideline 28. ETHICS AND DISSEMINATION: Ethical approval will not be necessary, as all data are already in the public domain. Results will be published in a peer-reviewed scientific journal. PROSPERO REGISTRATION NUMBER: CRD42021227888.