Effect of intraoperative PEEP with recruitment maneuvers on the occurrence of postoperative pulmonary complications during general anesthesia--protocol for Bayesian analysis of three randomized clinical trials of intraoperative ventilation

Abstract

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: We 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. We 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. Ethics and dissemination: The used data derive from approved studies that were published in recent years. The findings of this current analysis will be reported in a new manuscript, drafted by the writing committee on behalf of the three research groups. All investigators listed in the original trials will serve as collaborative authors.

Trial registration: ClinicalTrials.gov NCT03937375.

Keywords: Bayesian analysis; Mechanical ventilation; PEEP; intraoperative ventilation; postoperative pulmonary complications; recruitment maneuvers.

Figure 1.. Probability density distribution of the optimistic (purple), skeptic (black) and pessimistic (dark red) prior for the effect estimate in log-Odds scale.

Dotted lines show the mean estimate for each distribution. The skeptic prior is centered at 0 while optimistic and pessimistic prior are centered at -0.67 and 0.67 respectively.

Figure 2.. Probability density distribution for the heterogeneity prior.

Dotted lines separate heterogeneity categories based on τ values.

Figure 3.. Probability density distribution for the correlation prior.

Figure 4.. Posterior distribution of the log odds ratio (OR) fitting four logistic models using a “skeptical” prior with differing effect estimates: Panel A: slightly harmful, B: moderately harmful, C: moderately beneficial and D: strongly beneficial The distributions represent 100,000 draws from the posterior.

The black vertical line at 0 represents the point at which the OR is equal to 1 [i.e., log (OR) = 0]. The area to the right (in green) represents the probability that the intervention is harmful. The probability of severe harm [Pr (OR.1.25)] is shown in darker green. Probabilities for each posterior distribution are reported in the upper part of each panel. Values below 0 mean the intervention is beneficial [Pr (log(OR),0); Pr (OR,1.0)] and are shown in light yellow with the probability of benefit again in the upper part of the figure. The ROPE is defined as the OR between 1/1.1 and 1.1 (the segmented area around log (OR) = 0). The 95% High-Density Interval (HDI) is reported as a blue line in each panel. We can see how fitting logistic models with different simulated effect yields to different interpretation for instance there is considerable difference between panel A, where the probability of harm is 63% and the probability that the estimate fall in the ROPE is 68% and the HDI 95% crosses the 0 threshold and panel D, where the probability of benefit is 100% and the 95%HDI and ROPE do not overlap.