ArticleFrontiers in immunology2026
Development of a stacked ensemble model for risk stratification of chronic GVHD after allogeneic HSCT: Japanese nation-wide cohort study.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Effective risk stratification is vital for donor selection and treatment strategies in allogeneic hematopoietic stem cell transplantation (HSCT). We developed a prediction model for mid- to long-term outcomes after HSCT using a stacked ensemble model (SEM). Using data from the Japanese Transplant Registry Unified Management Program, we analyzed 14,430 patients alive without chronic GVHD (cGVHD) or relapse on day 100 after their first HSCT for hematologic malignancies between 2010 and 2018, predicting 24-month outcomes from pretransplant variables and posttransplant acute GVHD (aGVHD) information, including its treatment, accrued by days 30, 60, and 100. Data were randomly divided into training (80%) and validation (20%) sets, with 14 pretransplant risk factors as input variables. SEM achieved the highest C-index across evaluated endpoints, cGVHD, non-relapse mortality (NRM), and all-cause mortality (ACM), significantly exceeding the weaker learners for all endpoints and, using pretransplant factors, the strongest learner for NRM and ACM as well (both p=0.01), with a smaller, non-significant margin for cGVHD (C-index for cGVHD/NRM/ACM-SEM: 0.574/0.652/0.642, Cox-PH: 0.553/0.635/0.615, Random Survival Forest: 0.564/0.638/0.613, XGBoost: 0.556/0.633/0.627, Dynamic-DeepHit: 0.508/0.607/0.564). The C-index increased as posttransplant aGVHD information accrued (day 30: 0.581/0.655/0.649; day 60: 0.602/0.688/0.656; day 100: 0.606/0.696/0.664). Grade III to IV aGVHD showed predictive contribution to NRM, ultimately impacting ACM. Our SEM-based model offers a useful framework for predicting post-HSCT outcomes and highlights the critical impact of early aGVHD events on long-term prognosis.
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