Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection

•Optimal unrelated donor selection has the potential to improve the success rate of hematopoietic cell transplantation.•We developed a multivariable machine learning algorithm to improve donor selection.•Despite promising preliminary results, the algorithm failed in a pivotal study.•Translating machine learning risk predictors to clinical use remains a major challenge. The survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. To improve the donor selection process, we attempted to create an algorithm to quantify the likelihood of survival to 5 years after unrelated donor HCT for acute leukemia, based on the clinical characteristics of the donor selected. All standard clinical variables were included in the model, which also included average leukocyte telomere length of the donor based on its association with recipient survival in severe aplastic anemia, and links to multiple malignancies. We developed a multivariate classifier that assigned a Preferred or NotPreferred label to each prospective donor based on the survival of the recipient. In a previous analysis using a resampling method, recipients with donors labeled Preferred experienced clinically compelling better survival compared with those labeled NotPreferred by the test. However, in a pivotal validation study in an independent cohort of 522 patients, the overall survival of the Preferred and NotPreferred donor groups was not significantly different. Although machine learning approaches have successfully modeled other biological phenomena and have led to accurate predictive models, our attempt to predict HCT outcomes after unrelated donor transplantation was not successful.