Data collection and analysis
Data were collected retrospectively from the electronic and archived hospital medical records. We attempted to specifically identify the effects of VV-ECMO as a BTT on: posttransplant 30-day mortality and complications (need for postoperative ECMO, delayed chest closure, surgical re-exploration, tracheostomy, chest drainage within 24 hours, chest infection, sepsis, stroke, acute kidney injury [AKI] requiring renal replacement therapy [RRT]) and 1-year mortality.
Preoperative, intraoperative, and postoperative data are summarized for BTT and non-BTT patients. In the main analysis, both subgroups were submitted to optimal full matching based on Mahalanobis distance in respect to preoperative covariates. Based on their potential relevance to the observed outcomes and imbalance between the two subgroups, included covariates were age, gender, body mass index (BMI), serum creatinine and hemoglobin levels, platelet count <150x109/L and main diagnosis, with exact matching on the gender, low platelet count and underlying diagnosis (cystic fibrosis [CF] or “other”).19,20 We had no patients that required VV-ECMO as BTT among those with chronic obstructive pulmonary disease (COPD), emphysema, bronchiolitis, bronchiectasis, pulmonary hypertension and lymphangioleiomyomatosis. Therefore, in order to avoid aliasing between potential effects of VV-ECMO and diagnosis, a sensitivity analysis (using the same methodology) was performed including only diagnoses where at least one patient was bridged to LTx with VV-ECMO. To evaluate the effect of VV-ECMO as a BTT (vs. non-BTT), generalized mixed models (binary distribution, logit link; subclass as a random effect [cluster]) were fitted to each binary outcome with further adjustment for unbalanced covariates: frequentist (maximum likelihood estimation with Gauss-Hermite quadrature approximation; classical [sandwich] robust estimator) and Bayesian (4 chains, 4000 iterations, 8000 samples of the posterior, vaguely informative normal priors for ln[odds] and the intercept [0, 2.5; scaled], and priors on the terms of a decomposition of the covariance matrices [Gamma shape=1, scale=1; LKJ for correlation matrix, regularization=1; Dirichlet for the simplex vectors, concentration=1]). To evaluate the effect of VV-ECMO as a BTT on the chest drainage within the first 24 hours, data were ln-transformed (since right-skewed) and the same models, although with normal distribution and identity link, were fitted. We used packageMatchIt in R for matching,21 SAS 9.4 for Windows proc glimmix (SAS Inc., Cary, NC) for fitting frequentist and package rstanarm in R for Bayesian models.22 We evaluated susceptibility of the observed effects to unmeasured confounding by determining the E-value (packageEvalue in R).23 Despite a large number of analyzed outcomes and related formal statistical tests, we considered more appropriate not to implement multiplicity adjustments as adjustments of comparison-wise alpha could have resulted in falsely overlooked adverse effects of VV-ECMO as a BTT.