Risk Factors and Predictive Model of Poor Graft Function After Allogeneic Hematopoietic Stem Cell Transplantation
Ming Gao1, Yaqiong Tang1, Jiaqian Qi1
1National clinical research center for hematologic diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China; Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China; Key Laboratory of Thrombosis and Hemostasis of Ministry of Health, Suzhou, China.
Abstract:
Poor graft function (PGF) is a serious and life-threatening complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Current therapeutic approaches show suboptimal clinical outcomes, with substantial variability in treatment efficacy across studies, which highlights the critical need for timely identification of PGF and appropriate preventive strategies to improve prognosis. To identify PGF risk factors and establish a nomogram-based predictive model of PGF, this real-world study analyzed 795 hematological malignancies patients underwent allo-HSCT. We identified lower CD34+ cell dose in the graft, infection, cytomegalovirus activation, splenomegaly and anti-HLA antibody as independent risk factors for PGF. Severe graft-versus-host disease exhibited significant association with secondary PGF in subgroup analysis. Recipients with good graft function demonstrated higher 3-year overall survival (OS) rate compared to PGF cohorts (78.1% versus 50.6%). Notably, secondary PGF cases exhibited superior 3-year OS relative to primary PGF cases (52.0% versus 46.2%), and the primary PGF group showed a higher cumulative incidence of NRM than secondary PGF group. Restricted cubic spline analysis demonstrated a dose-dependent relationship between infused stem cell doses and the risk of PGF. Both CD34+ cell dose and MNC dose showed inverse associations with the predicted probability of PGF. To assess the relative contribution of each covariate to PGF risk stratification, we constructed a predictive model based on the nomogram framework. Ten clinically relevant predictors were incorporated into the model, which demonstrated strong predictive performance and excellent calibration. The model also exhibited robust discriminative capacity (C-statistics = .782) and provided meaningful clinical utility. Importantly, this predictive tool facilitates identification of patients at high risk of PGF who could benefit from preventive measures.
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