Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Decisions about managing patients on the heart transplant waitlist are
currently made by committees of doctors who consider multiple factors, but the
process remains largely ad-hoc. With the growing volume of longitudinal
patient, donor, and organ data collected by the United Network for Organ
Sharing (UNOS) since 2018, there is increasing interest in analytical
approaches to support clinical decision-making at the time of organ
availability. In this study, we benchmark machine learning models that leverage
longitudinal waitlist history data for time-dependent, time-to-event modeling
of waitlist mortality. We train on 23,807 patient records with 77 variables and
evaluate both survival prediction and discrimination at a 1-year horizon. Our
best model achieves a C-Index of 0.94 and AUROC of 0.89, significantly
outperforming previous models. Key predictors align with known risk factors
while also revealing novel associations. Our findings can support urgency
assessment and policy refinement in heart transplant decision making.
