a Bayesian Analysis of Three Randomised Clinical Trials of Intraoperative Ventilation

The investigators designed a protocol for a Bayesian unplanned posthoc analysis using the pooled dataset from three large randomized clinical trials. The primary endpoint will be a composite of postoperative pulmonary complications (PPC) within the first seven postoperative days, which reflects the primary endpoint of the original studies. The investigators will carry out a reanalysis of the harmonised database using Bayesian statistics.

Background: Using the frequentist approach, a recent meta-analysis of three randomized clinical trials in patients undergoing intraoperative ventilation during general anesthesia for major surgery. failed to show the benefit of ventilation that uses high positive end expiratory pressure with recruitment maneuvers when compared to ventilation that uses low positive end-expiratory pressure without recruitment maneuvers. Methods: The investigators designed a protocol for a Bayesian analysis using the pooled dataset. The multilevel Bayesian logistic model will use the individual patient data. Prior distributions will be prespecified to represent a varying level of skepticism for the effect estimate. The primary endpoint will be a composite of postoperative pulmonary complications (PPC) within the first seven postoperative days, which reflects the primary endpoint of the original studies. The investigators preset a range of practical equivalence to assess the futility of the intervention with an interval of odds ratio (OR) between 0.9 and 1.1 and assess how much of the 95% of highest density interval (HDI) falls between the region of practical equivalence.