Prognostic factor analysis of CD19 CAR-T therapy followed by hematopoietic stem cell transplantation in relapsed/refractory B-ALL.

Authors

Xin Wang, Linlin Liu, Yuanyuan Zhai, Jianmei Xu, Jile Liu, Hairong Lyu, Mingfeng Zhao

Published in

Clinical and experimental medicine. Volume 26. Issue 1. Jul 30, 2026. Epub Jul 30, 2026.

Abstract

To evaluate the long-term prognosis of patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) who received sequential allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CD19 Chimeric Antigen Receptor T-cell (CAR-T) therapy, and to identify the risk factors influencing overall survival (OS) and the severity of acute graft-versus-host disease (aGVHD). A retrospective analysis was conducted on 63 patients with R/R B-ALL who received sequential allo-HSCT after CD19 CAR-T therapy. Cox regression analysis was used to identify factors influencing OS. Patients were stratified according to the severity of aGVHD (low-grade vs. high-grade). Logistic regression was employed to identify predictors of severe aGVHD, and a nomogram was constructed based on these predictors. The model was internally validated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The two-year OS and progression-free survival (PFS) rates were 67.7% and 55.6%, respectively.The time interval from CAR-T therapy to transplantation, along with high-grade aGVHD, were identified as independent risk factors for OS, whereas low-grade aGVHD exerted a protective effect. A longer time interval from CAR-T therapy to transplantation and pre-transplant minimalresidual disease (MRD)-positive status were independent predictors of severe aGVHD. The nomogram developed for predicting the severity of aGVHD demonstrated good discrimination (areaunder the curve [AUC] = 0.827), satisfactory calibration, and clinical utility upon internal validation. We identified key prognostic factors and developed a validated nomogram that enables early, individualized prediction of severe aGVHD in this setting. This model can assist in early risk stratification and targeted intervention to improve outcomes.

PMID:
42606754
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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