Challenging a clinical dogma with multimodal machine learning: a retrospective analysis of transplant mismatched donor selection

In the modern era of post-transplantation cyclophosphamide (PTCy), clinicians frequently face a choice between the two most common mismatched donor options: haploidentical donors and mismatched unrelated donors (MMUD) [ 1 ]. While PTCy attenuates the risks of Human Leukocyte Antigen (HLA) disparity, [ 2 , 3 ] it shifts the decision-making calculus toward non-HLA factors like donor age. However, the prevailing “younger is better” maxim often drives algorithms to prioritize younger donors regardless of donor type, potentially causing unnecessary delays. Current evidence regarding the specific impact and the absolute magnitude of donor age in the haploidentical versus MMUD setting is conflicting, with disparate age thresholds and inconsistent conclusions regarding survival and non-relapse mortality (NRM). [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] Moreover, emerging data suggest that the impact of donor age is non-linear and context-dependent [ 14 ], implying that the arbitrary dichotomizations inherent to conventional regression models may be insufficient to capture these complex dynamics. To address this, we applied multimodal machine learning techniques to a Center for International Blood and Marrow Transplant Research cohort to quantify the precise association of donor age with outcomes after haploidentical and MMUD transplantation.