Using T-lymphocyte subsets at engraftment to predict the risk of acute graft-versus-host disease in patients with thalassemia major: development of a new predictive nomogram - Hongwen Xiao; Gaohui Yang; Qiulin Huang; Zhenbin Wei; Zhaoping Gan; Meiqing Wu; Zeyan Shi; Huicheng Huang; Zhaofang Pan; Lianjin Liu; Lingling Shi; Zhongming Zhang; et al. (Therapeutic Advances in Hematology)
Background: Acute graft-versus-host disease (aGvHD) is the primary cause of mortality following allogeneic hematopoietic cell transplantation (HCT). Objectives: This study aimed to predict the risk of aGvHD after HCT in patients with thalassemia major using a novel predictive nomogram. Design: A retrospective study was used to develop the prediction model. Methods: We performed retrospective analyses on 402 consecutive thalassemia patients who underwent HCT. Risk factors for aGvHD were analyzed using Cox proportional regression models. T-lymphocyte subsets were collected from 240 patients at the time of neutrophil engraftment. Least Absolute Shrinkage and Selection Operator regression was utilized to screen the indices, with cut-off values established through restricted cubic spline (RCS) regression. The predictive model was developed by integrating these T-lymphocyte subsets with clinical features, aiming to enhance the accuracy of aGvHD risk prediction. Results: Among 402 thalassemia patients analyzed post-transplantation, significant independent risk factors for aGvHD included matched unrelated donors, haploid-related donors, peripheral blood stem cell infusions, and donor age older than 40 years. Our RCS analysis indicated a marked increase in aGvHD risk when CD4+ T-cell counts exceeded 36 cells/μL and CD8+ T-cell counts exceeded 43 cells/μL during neutrophil engraftment. The integration of T-lymphocyte subsets with clinical risk factors into a Cox regression model demonstrated good predictive performance for assessing aGvHD risk. Conclusion: This study presents a novel model designed to predict aGvHD in thalassemia patients post-transplantation by utilizing T-lymphocyte data at the time of engraftment. The model facilitates the creation of personalized treatment plans, aiming to minimize the incidence of aGvHD and improve patient outcomes.