Introduction: Global rank scores are increasingly used in clinical trials. The approach permits combinations of binary, surrogate or continuous outcomes in a composite endpoint with components ranked by clinical relevance. We sought to develop a global rank endpoint reflecting post-cardiopulmonary bypass (CPB) outcomes in neonates, and evaluated power gains when using this approach in simulated trials.Methods: Endpoints for the global rank composite were selected from variables in the STS Congenital Heart Surgery Database (STS-CHSD). Monte Carlo trial simulations (n=50,000) were performed using STS-CHSD data for neonates undergoing CPB surgery (2010-2016, 11,408 surgeries, 127 centers). Neonates (1200 per simulation) were randomly selected from the STS-CHSD and assigned 1:1 to placebo vs treatment. Rank outcomes were allocated based on prevalence in the study population with odds = 0.7 for treatment vs placebo. We evaluated power based on the proportion of trial datasets with a significant outcome (p<0.05), and evaluated the impact of covariate adjustment using the STS-CHSD risk model (including patient factors, preop risk factors and surgical complexity score).Results: Overall discharge mortality was 9.1% and the unranked composite mortality / morbidity endpoint occurred in 35.7% (Table). Prevalence of the composite increased with surgical complexity (22.9% for STAT ≤ 3; 29.8% for STAT 4; 50.3% for STAT 5 operations). In trial simulations, study power was 38% for a mortality only endpoint, 74% for the unranked composite, and increased to 77% when endpoint components were ranked. With addition of post-op length of stay, power increased to 89% with a further increase to 94% with covariate adjustment.Conclusions: Our simulations demonstrate that the global rank endpoint, as well as covariate adjustment increase study power, improving trial feasibility. With prospective validation, our rank measure could be applied to trials focused on post-CPB outcomes.