Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes

Abstract: We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). NFT BART models were trained on data from 10 016 patients who underwent a first HLA-A, B, C, and DRB1 matched unrelated donor (MUD) HCT between 2016 and 2019, reported to the Center for International Blood and Marrow Transplant Research, then validated on an independent cohort of 1802 patients. The NFT BART models were adjusted based on recipient, disease, and transplant variables. We defined a clinically meaningful impact on overall survival (OS) or event-free survival (EFS; survival without relapse, graft failure, or moderate to severe chronic graft-versus-host disease) as >1% difference in predicted outcome at 3 years. Characteristics with