ArticleNPJ digital medicine2026
Development and explainable AI-driven characterization of a prognostic model for haploidentical transplantation outcomes.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- The Pitfalls of Categorizing Donor Age: Arbitrary Cutoffs Fail to Predict Survival in Haploidentical HCT.American journal of hematology · 2026Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.