Development and explainable AI-driven characterization of a prognostic model for haploidentical transplantation outcomes

We developed a novel, explainable artificial intelligence (AI)-driven prognostic model using a contemporary single-centre cohort of 668 patients undergoing haploidentical hematopoietic cell transplantation (HCT) with post-transplantation cyclophosphamide (PTCy) prophylaxis (2015-2024) to define the hierarchy of prognostic factors for overall survival. Employing a gradient boosting machine with explainable AI techniques, including partial dependence plots and surrogate decision trees, revealed a non-linear, U-shaped effect of donor age, with optimal outcomes for donors in their late 20 s to early 40 s. Recipient age was the dominant risk factor, modulated by donor age and HLA factors. HLA-DPB1 non-permissive mismatches were the most significant HLA factor (9.6% 3-year OS reduction), followed by HLA-B leader and HLA-DQB1 mismatches, with no independent effect of HLA-DRB1. The model accurately stratified patients into distinct risk quartiles, with 3-year survival ranging from 75% in the lowest-risk to below 20% in the highest-risk group. Critically, simulation analyses demonstrated that optimal donor selection (age 30, no key HLA mismatches) could shift a patient's risk profile by approximately one full quartile, significantly improving survival (e.g., Q3 patients from 20% to 50% 3-year OS). This study provides a practical, data-driven framework for personalized donor selection in the modern HCT era. Future multi-center validation studies are warranted.