A machine learning model generated by a team from the European Society for Blood and Marrow Transplantation (EBMT) outperformed standard statistical models in identifying and stratifying transplant risk for patients with myelofibrosis, according to new research published today in Blood, the American Society of Hematology’s flagship journal.
“Although there are many models available to identify patients with high-risk myelofibrosis, we are still lacking tools to determine the risk of transplant for these patients,” said one of the study’s lead authors, Juan Carlos Hernández Boluda, MD, PhD, a hematologist at the Hospital Clínico of Valencia and lead of the myeloproliferative neoplasms committee within the EBMT Chronic Malignancies Working Party. “Our prognostic tool comprehensively and effectively…