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.

翻译：目前，对心脏移植等待名单患者的管理决策由医生委员会根据多种因素制定，但这一过程在很大程度上仍依赖于临时判断。自2018年以来，美国器官共享联合网络（UNOS）收集的纵向患者、供体和器官数据不断增加，促使学界对在器官可用时支持临床决策的分析方法产生了日益浓厚的兴趣。在本研究中，我们以纵向等待记录历史数据为基础，对用于时间依赖性时间-事件建模的机器学习模型进行了基准测试，以预测等待名单死亡率。我们使用包含77个变量的23,807份患者记录进行训练，并评估了1年时间范围内的生存预测和判别性能。我们的最优模型达到了0.94的C指数和0.89的AUROC，显著优于既往模型。关键预测因子既与已知风险因素一致，也揭示了新的关联性。研究结果可为心脏移植决策中的紧迫性评估和政策优化提供支持。