Benchmarking Waitlist Mortality Prediction Through Time-to-Event Modeling using New UNOS Dataset

Presentation Time: 02:36 PM - 02:48 PM

Abstract Keywords: Clinical Decision Support, Machine Learning, Data Mining
Primary Track: Applications

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 model-based approaches to support clinical decision-making. In this study, we benchmark machine learning models that leverage dynamic waitlist history data for time-dependent, time-to-event modeling of waitlist mortality. We train on 23,807 patient records with 71 variables and evaluate both survival prediction and discrimination at a 1-year horizon. Our best model achieves a C-index of 0.94 and AUC of 0.89, significantly outperforming previous static models. Key predictors align with known clinical risk factors while also revealing novel associations. Our findings can support urgency assessment and policy refinement in heart transplant allocation.

Speaker(s):
Yingtao Luo, Ph.D.
Carnegie Mellon University

Author(s):
Yingtao Luo, Ph.D. - Carnegie Mellon University; Reza Skandari, Ph.D. - Imperial College, London; Carlos Martinez - United Network for Organ Sharing; Arman Kilic, M.D. - Medical University of South Carolina; Rema Padman, PhD - Carnegie Mellon University;