ArticleBlood advances2024
Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.
Article in Blood advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- HLA matching in contemporary haematopoietic cell transplantation: Recommendations from the EBMT Practice Harmonisation and Guidelines Committee.Bone marrow transplantation · 2026Guideline
- Post-Transplant Cyclophosphamide-Based Graft-Versus-Host Disease Prophylaxis After Mismatched Unrelated Donor Peripheral Blood Stem Cell Transplantation.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025Trial
- Are all mismatched donors the same in allogeneic hematopoietic cell transplantation for secondary acute myeloid leukemia in the posttransplant cyclophosphamide era?Bone marrow transplantation · 2026Article
- Chronic graft-versus-host disease.Nature reviews. Disease primers · 2026Review
- Impact of donor age on cognitive recovery in allogenic hematopoietic stem cell transplant patients with mild cognitive impairment.Bone marrow transplantation · 2026Article
- Optimal donor selection for transplant to reduce GVHD risk and augment graft-versus-malignancy efficacy.Current opinion in immunology · 2026Review
- Article
- Development and explainable AI-driven characterization of a prognostic model for haploidentical transplantation outcomes.NPJ digital medicine · 2026Article
- Article
- Matched unrelated vs haploidentical donor hematopoietic cell transplantation using posttransplant cyclophosphamide.Blood advances · 2026Article
- Allogeneic Hematopoietic Cell Donor Selection: Contemporary Guidelines from the NMDP/CIBMTR.Transplantation and cellular therapy · 2025Review
- Unrelated Donor Age and Recipient Outcomes After Posttransplant Cyclophosphamide vs Conventional Prophylaxis.JAMA oncology · 2025Article
- Artificial intelligence in hematology: current trends and application areas.Annals of hematology · 2025Review
- Comparative outcomes of HSCT with posttransplant cyclophosphamide in older adults: the impact of donor type.Blood advances · 2025Article
- Choosing Between HLA-Mismatched Unrelated and Haploidentical Donors: Donor Age Considerations.Transplantation and cellular therapy · 2025Article
- Role of Donor Cytomegalovirus Serostatus in Cytomegalovirus-Seronegative Recipients of Unrelated Donor Hematopoietic Cell Transplantation with Post-Transplant Cyclophosphamide Prophylaxis.Transplantation and cellular therapy · 2025Article
- Is transplant success linked to donor CMV serostatus?Blood advances · 2025Article
- Hematopoietic Stem Cell Transplantation: Recent Advances.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
abstractWe 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 <1% impact (within a zone of indifference) were not considered to be clinically relevant. Donor cytomegalovirus, 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, in which, compared with 18-year-old donors, donors aged ≥31 years old were associated with lower OS. Both donor age (≤32 years) and use of a male donor, regardless of recipient sex, improved EFS. We, therefore, recommend selecting the earliest available donor within the 18 to 30 years age range for HCT to optimize OS. If several donors in the 18 to 30 years age range are available, a male donor may be chosen to optimize EFS.
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