用纵向数据建模心脏移植等待者死亡风险，提升决策科学性。
Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
- 基于UNOS长期数据，构建时间依赖的生存预测模型。
- 1年生存预测C-指数达0.94，优于以往模型。
- 识别已知风险因素及新关联，助力临床决策与政策优化。
目前心脏移植等待名单患者的管理依赖医生团队综合判断，过程较主观。自2018年起，美国器官共享网络（UNOS）积累了大量患者、供体与器官的纵向数据，推动了基于数据分析的临床支持方法发展。本研究针对等待名单患者的死亡风险，采用时间依赖型时间-事件建模，评估机器学习模型在纵向历史数据上的表现。模型基于23,807例患者记录与77个变量进行训练，评估1年期的生存预测与区分能力。最佳模型在1年时达到C-指数0.94和AUROC 0.89，显著优于现有方法。关键预测因子与已知风险因素一致，同时揭示新的关联。研究结果可为移植紧迫性评估与政策制定提供依据。
原文摘要 · Abstract (English)
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.
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