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

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 HCT between 2016 and 2019 reported to the CIBMTR, then validated on an independent cohort of 1,802 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 GVHD) as >1% difference in predicted outcome at 3 years. Characteristics with <1% impact (within a zone of indifference) were not considered to be clinically relevant. Donor CMV, parity, HLA-DQB1 and HLA-DPB1 T cell epitope matching fell within the zone of indifference. The only significant donor factor that associated with OS was age, where compared to 18-year-old donors, donors ≥31 years old were associated with lower OS. Both donor age (≤ 32 years old) and use of a male donor, regardless of recipient sex, improved EFS. We therefore recommend selecting the earliest available donor within the 18-30 age range for HCT to optimize OS. If several donors in the 18-30-year-old age range are available, a male donor may be chosen to optimize EFS.
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