TY - JOUR
T1 - Validation of a Hematopoietic Cell Transplant-Composite Risk (HCT-CR) Model for Post-Transplant Survival Prediction in Patients with Hematologic Malignancies
AU - Ciurea, Stefan O.
AU - Kongtim, Piyanuch
AU - Hasan, Omar
AU - Ramos Perez, Jorge M.
AU - Torres, Janet
AU - Rondon, Gabriela
AU - Champlin, Richard E.
N1 - Publisher Copyright:
©2020 American Association for Cancer Research.
PY - 2020/5/15
Y1 - 2020/5/15
N2 - 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.
AB - 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.
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U2 - 10.1158/1078-0432.CCR-19-3919
DO - 10.1158/1078-0432.CCR-19-3919
M3 - Article
C2 - 32019857
AN - SCOPUS:85084920325
SN - 1078-0432
VL - 26
SP - 2404
EP - 2410
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 10
ER -