Dr Hernández-Boluda on an EBMT Machine Learning Model for Identifying and Stratifying Transplant Risk in Myelofibrosis

Juan Carlos Hernández-Boluda, MD, PhD, an associated physician at the Hematology and Medical Oncology Service of the Hospital Clínico of Valencia, discussed an EBMT machine learning–based model for identifying and stratifying transplant risk for patients with myelofibrosis.

On March 27, 2025, a team from the European Society for Blood and Marrow Transplantation (EBMT) announced that a machine learning model they had developed for identifying and stratifying transplant risk for patients with myelofibrosis outperformed standard statistical models. The machine learning approach was trained using records of 3887 patients with myelofibrosis who received their allogeneic hematopoietic stem cell transplantation (allo-HCT) between 2005 and 2020. Data from 1296 patients were used to assess and validate the model.

The open-access tool outperformed standard models in terms of accuracy, identifying a subset of patients with high-risk disease who had a 40% chance of dying within 1 year of transplant and a non-relapse mortality rate of approximately 35%. Moreover, the model identified 25% of patients as being a part of this group at a high-risk for poor outcomes after transplantation, compared with 10.1% via a Cox-derived score and 8.2% using the Center for International Blood and Marrow Transplant Research model.

When determining if a patient with myelofibrosis is a good candidate for allo-HCT, investigators must first determine if the patient falls into the high-risk category using standardized prognostic scoring systems, Hernández-Boluda explained. However, currently available approaches are not very good at predicting the toxicity of the transplant for the patient, he continued. The machine learning tool will be able to provide both risk stratification information and transplantation toxicity risk, he said.

The machine learning approach can also visualize the transplantation risk, and this image could be shared with patients to better inform them of their risk category, Hernández-Boluda said. Combining previous approaches with the machine learning tool will help clinicians make a better-informed decision regarding allo-HCT, he concluded.