Stefan O Ciurea 1 , Piyanuch Kongtim 2,3 , Omar Hasan 2 , Jorge M Ramos Perez 2 , Janet Torres 2 , Gabriela Rondon 2 , Richard E Champlin 2 Show Affiliations »
Abstract
PURPOSE: Allogeneic hematopoietic stem cell transplantation (AHCT) outcomes depend on disease and patient characteristics. We previously developed a novel prognostic model, hematopoietic cell transplant composite-risk (HCT-CR) by incorporating the refined disease risk index (DRI-R) and hematopoietic cell transplant-comorbidity/age index (HCT-CI/Age) to predict post-transplant survival in patients with acute myeloid leukemia and myelodysplastic syndrome. Here we aimed to validate and prove the generalizability of the HCT-CR model in an independent cohort of patients with hematologic malignancies receiving AHCT. EXPERIMENTAL DESIGN: Data of consecutive adult patients receiving AHCT for various hematologic malignancies were analyzed. Patients were stratified into four HCT-CR risk groups. The discrimination, calibration performance, and clinical net benefit of the HCT-CR model were tested. RESULTS: The HCT-CR model stratified patients into four risk groups with significantly different overall survival (OS). Three-year OS was 67.4%, 50%, 37.5%, and 29.9% for low, intermediate, high, and very high-risk group, respectively (P < 0.001). The HCT-CR model had better discrimination on OS prediction when compared with the DRI-R and HCT-CI/Age (C-index was 0.69 vs. 0.59 and 0.56, respectively, P < 0.001). The decision curve analysis showed that HCT-CR model provided better clinical utility for patient selection for post-transplant clinical trial than the "treat all" or "treat none" strategy and the use of the DRI-R and HCT-CI/Age model separately. CONCLUSIONS: The HCT-CR can be effectively used to predict post-transplant survival in patients with various hematologic malignancies. This composite model can identify patients who will benefit the most from transplantation and helps physicians in making decisions regarding post-transplant therapy to improve outcomes. ©2020 American Association for Cancer Research.
PURPOSE: Allogeneic hematopoietic stem cell transplantation (AHCT) outcomes depend on disease and patient characteristics. We previously developed a novel prognostic model, hematopoietic cell transplant composite-risk (HCT-CR) by incorporating the refined disease risk index (DRI-R) and hematopoietic cell transplant-comorbidity/age index (HCT-CI/Age) to predict post-transplant survival in patients with acute myeloid leukemia and myelodysplastic syndrome . Here we aimed to validate and prove the generalizability of the HCT-CR model in an independent cohort of patients with hematologic malignancies receiving AHCT. EXPERIMENTAL DESIGN: Data of consecutive adult patients receiving AHCT for various hematologic malignancies were analyzed. Patients were stratified into four HCT-CR risk groups. The discrimination, calibration performance, and clinical net benefit of the HCT-CR model were tested. RESULTS: The HCT-CR model stratified patients into four risk groups with significantly different overall survival (OS). Three-year OS was 67.4%, 50%, 37.5%, and 29.9% for low, intermediate, high, and very high-risk group, respectively (P < 0.001). The HCT-CR model had better discrimination on OS prediction when compared with the DRI-R and HCT-CI/Age (C-index was 0.69 vs. 0.59 and 0.56, respectively, P < 0.001). The decision curve analysis showed that HCT-CR model provided better clinical utility for patient selection for post-transplant clinical trial than the "treat all" or "treat none" strategy and the use of the DRI-R and HCT-CI/Age model separately. CONCLUSIONS: The HCT-CR can be effectively used to predict post-transplant survival in patients with various hematologic malignancies . This composite model can identify patients who will benefit the most from transplantation and helps physicians in making decisions regarding post-transplant therapy to improve outcomes. ©2020 American Association for Cancer Research.
Entities: Disease
Species
Year: 2020
PMID： 32019857 DOI： 10.1158/1078-0432.CCR-19-3919
Source DB: PubMed Journal: Clin Cancer Res ISSN： 1078-0432 Impact factor: 12.531