Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection - Buturovic, Ljubomir (Biol Blood Marrow Transplant, 2018)
Survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. In order to improve the donor selection process, we attempted to create an algorithm to quantify the likelihood of survival to five 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 (ATL) 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 prior analysis using a resampling method, recipients whose donors were labeled Preferred experienced clinically compelling better survival compared to donors labeled as NotPreferred by the test(1). 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 biologic phenomena and led to accurate predictive models, our attempt to predict HCT outcomes after unrelated donor transplantation was not successful.