ECMO, ARDS and meta-analyses: Bayes to the rescue?

Purpose: A recent meta-analysis by Munshi et al. (Lancet Respiratory Medicine, 2019) claimed mortality treatment efficacy for extra corporeal membrane oxygenation (ECMO) in the acute respitratory syndrome (ARDS) despite very low meta-analytic study numbers (n = 2 (RCTs), risk-ratio (RR) 0·73 (95%CI: 0·58–0·92); n = 5 (2 RCT, 3 observational), RR 0·69 (95%CI: 0·50–0·95)). We explore this efficacy claim by a comprehensive re-analysis of the data. Methods: Data were sourced from the two- and five-study meta-analyses, conducted using the Der-Simonian & Laird (DSL) method. A variety of frequentist (DSL, restricted maximum likelihood (REML), Paul-Mandel (PM), with/without Hartung-Knapp-Sidik-Jonkman variance correction), a beta-binomial model (BBN)) and Bayesian models (2 finite-mixture and several Markov-Chain-Monte-Carlo) were used to estimate treatment effects. Fragility-indices, the minimum patients changing mortality outcome needed to induce a conclusion change were also applied. Results: For the 2-study and 5-study meta-analysis only the uncorrected frequentist estimators (DSL, REML, PM) demonstrated significant RR. Except for the BBN model, which was significant for the 2-study meta-analysis, intervals for all other models included the null. Both meta-analyses demonstrated fragility. Conclusions: Having canvassed the conduct of both meta-analyses presented by Munshi et al. and proffered alternative methods, we find no certainty regarding the efficacy of ECMO in ARDS.