Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis

• A machine learning model, available as an interactive web application, was created to predict survival after transplant in myelofibrosis.

• This tool is a step toward personalized medicine, enabling the identification of 25% of patients with poor transplantation outcomes.

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5,183 MF patients who underwent first allo-HCT between 2005 and 2020 at EBMT centers, we examined different machine learning (ML) models to predict overall survival (OS) after transplant. The cohort was divided into a training set (75%) and a test set (25%) for model validation. A Random Survival Forests (RSF) model was developed based on 10 variables: patient age, comorbidity index, performance status, blood blasts, hemoglobin, leukocytes, platelets, donor type, conditioning intensity, and graft-versus-host disease prophylaxis. Its performance was compared with a four-level Cox regression-based score and other ML-based models derived from the same dataset, and with the CIBMTR score. The RSF outperformed all comparators, achieving better concordance indices across both primary and post-essential thrombocythemia/polycythemia vera MF subgroups. The robustness and generalizability of the RSF model was confirmed by Akaike's Information Criterion and time-dependent Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) metrics in both sets. While all models were prognostic for non-relapse mortality, the RSF provided better curve separation, effectively identifying a high-risk group comprising 25% of patients. In conclusion, ML enhances risk stratification in MF patients undergoing allo-HCT, paving the way for personalized medicine. A web application (https://gemfin.click/ebmt) based on the RSF model offers a practical tool to identify patients at high risk for poor transplantation outcomes, supporting informed treatment decisions and advancing individualized care.