Roni Shouval1,2,3, Myriam Labopin4,5, Norbert C Gorin4,5, David Bomze1,2, Mohamed Houhou5, Didier Blaise6, Tsila Zuckerman7, Gabriela M Baerlocher8, Saveria Capria9, Edouard Forcade10, Anne Huynh11, Riccardo Saccardi12, Massimo Martino13, Michel Schaap14, Depei Wu15, Mohamad Mohty4,5, Arnon Nagler1,2,5. 1. Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center at Tel HaShomer, Ramat-Gan, Israel. 2. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 3. Dr. Pinchas Bornstein Talpiot Medical Leadership Program, Chaim Sheba Medical Center at Tel HaShomer, Ramat-Gan, Israel. 4. Department of Hematology and Cell Therapy, Saint-Antoine Hospital, Paris, France. 5. Acute Leukemia Working Party of the European Society for Blood and Marrow Transplantation, Saint-Antoine Hospital, Paris, France. 6. Transplantation and Cell Therapy Program, Marseille Cancer Research Center, Paoli Calmettes Institute, Marseille, France. 7. Department of Hematology, Rambam Medical Center, Haifa, Israel. 8. Department of Hematology, Inselspital, Bern University Hospital, University of Bern, Switzerland. 9. Division of Hematology, Sapienza University, Rome, Italy. 10. Service Hématologie Clinique et Thérapie Cellulaire, Centre Hospitalier Universitaire de Bordeaux Hôpital Haut-Leveque, Pessac, France. 11. Department of Hematology, Institut Universitaire du Cancer Toulouse Oncopole, Toulouse, France. 12. Department of Cellular Therapies and Transfusion Medicine, Careggi University Hospital, Firenze, Italy. 13. Stem Cell Transplant Unit, Hemato-Oncology Department, Grande Ospedale Metropolitano Bianchi Melacrino Morelli, Reggio Calabria, Italy. 14. Department of Hematology, Radboud University Medical Centre, Nijmegen, the Netherlands. 15. First Affiliated Hospital of Soochow University, Suzhou, China.
Abstract
BACKGROUND: Autologous stem cell transplantation (ASCT) is a potential consolidation therapy for acute myeloid leukemia (AML). This study was designed to develop a prediction model for leukemia-free survival (LFS) in a cohort of patients with de novo AML treated with ASCT during their first complete remission. METHODS: This was a registry study of 956 patients reported to the European Society for Blood and Marrow Transplantation. The primary outcome was LFS. Multivariate Cox regression modeling with backward selection was used to select variables for the construction of the nomogram. The nomogram's performance was evaluated with discrimination (the area under the receiver operating characteristic curve [AUC]) and calibration. RESULTS: Age and cytogenetic risk (with or without FMS-like tyrosine kinase 3 internal tandem duplication) were predictive of LFS and were used for the construction of the nomogram. Each factor in the nomogram was ascribed points according to its predictive weight. Through the calculation of the total score, the probability of LFS at 1, 3, and 5 years for each patient could be estimated. The discrimination of the nomogram, measured as the AUC, was 0.632 (95% confidence interval [CI], 0.595-0.669), 0.670 (95% CI, 0.635-0.705), and 0.687 (95% CI, 0.650-0.724), respectively. Further validation with bootstrapping showed similar AUCs (0.629 [95% CI, 0.597-0.657], 0.667 [95% CI, 0.633-0.699], and 0.679 [95% CI, 0.647-0.712], respectively), and this suggested that the model was not overfitted. Calibration was excellent. Patients were stratified into 4 incremental 5-year prognostic groups, with the probabilities of LFS and overall survival ranging from 25% to 64% and from 33% to 79%, respectively. CONCLUSIONS: The Auto-AML nomogram score is a tool integrating individual prognostic factors to provide a probabilistic estimation of LFS after ASCT for patients with AML.
BACKGROUND: Autologous stem cell transplantation (ASCT) is a potential consolidation therapy for acute myeloid leukemia (AML). This study was designed to develop a prediction model for leukemia-free survival (LFS) in a cohort of patients with de novo AML treated with ASCT during their first complete remission. METHODS: This was a registry study of 956 patients reported to the European Society for Blood and Marrow Transplantation. The primary outcome was LFS. Multivariate Cox regression modeling with backward selection was used to select variables for the construction of the nomogram. The nomogram's performance was evaluated with discrimination (the area under the receiver operating characteristic curve [AUC]) and calibration. RESULTS: Age and cytogenetic risk (with or without FMS-like tyrosine kinase 3 internal tandem duplication) were predictive of LFS and were used for the construction of the nomogram. Each factor in the nomogram was ascribed points according to its predictive weight. Through the calculation of the total score, the probability of LFS at 1, 3, and 5 years for each patient could be estimated. The discrimination of the nomogram, measured as the AUC, was 0.632 (95% confidence interval [CI], 0.595-0.669), 0.670 (95% CI, 0.635-0.705), and 0.687 (95% CI, 0.650-0.724), respectively. Further validation with bootstrapping showed similar AUCs (0.629 [95% CI, 0.597-0.657], 0.667 [95% CI, 0.633-0.699], and 0.679 [95% CI, 0.647-0.712], respectively), and this suggested that the model was not overfitted. Calibration was excellent. Patients were stratified into 4 incremental 5-year prognostic groups, with the probabilities of LFS and overall survival ranging from 25% to 64% and from 33% to 79%, respectively. CONCLUSIONS: The Auto-AML nomogram score is a tool integrating individual prognostic factors to provide a probabilistic estimation of LFS after ASCT for patients with AML.
Authors: Yousra El Alaoui; Adel Elomri; Marwa Qaraqe; Regina Padmanabhan; Ruba Yasin Taha; Halima El Omri; Abdelfatteh El Omri; Omar Aboumarzouk Journal: J Med Internet Res Date: 2022-07-12 Impact factor: 7.076