发表机构
Case Western Reserve University; University of Toledo（凯斯西储大学; 托莱多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对 deceased donor 肾移植的存活预测，提出基于配对受者的评估框架，发现五类模型均达约60%准确率，且该指标比C-index更具临床相关性。
AI 中文摘要
利用机器学习算法预测肾移植结局（如移植物不可避免衰竭前的年数）已引起广泛关注，这类预测算法或可用于移植前供受者匹配，以识别更适配的供受者，进而改善移植后结局。本研究探索了基于移植受者科学登记处（SRTR）的 deceased donor 肾移植数据训练存活预测模型的应用；提出了一种新颖的基于配对受者的评估框架，该框架对比了从同一 deceased donor 获得肾脏的两名受者的移植物结局，使我们能够评估为特定供体更换受者的反事实收益。研究发现，从线性模型到深度学习模型的五种不同复杂度的存活预测模型，均达到约 60% 的基于配对受者的准确率；我们进一步将该准确率转化为可解释的移植后获益年数。此外，本研究强调了常用的一致性指数（C-index）指标在该场景下评估存活预测准确率的主要局限性，并证明所提出的基于配对受者的准确率指标更具临床相关性，能更好地反映现实世界的分配场景。
英文摘要
There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes. In this study, we explore the use of survival prediction models trained on deceased donor kidney transplant data from the Scientific Registry of Transplant Recipients (SRTR). We propose a novel paired recipient-based evaluation framework that compares graft outcomes between two recipients who received kidneys from the same deceased donor, allowing us to evaluate the counterfactual benefit of changing the recipient for a certain donor. We find that five different survival prediction models, ranging in complexity from linear to deep learning-based models, all result in ~60% paired recipient-based accuracy. We further translate this accuracy into an interpretable quantity of post-transplant years gained. We also highlight major limitations of the commonly used concordance index (C-index) metric for evaluating survival prediction accuracy in this setting and demonstrate that our proposed paired recipient-based accuracy metric is more clinically relevant and better reflects real-world allocation settings.
CommentsTo appear at the Machine Learning for Healthcare Conference (MLHC) 2026
