Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants

2026-08-03

AI Summary

Existing survival prediction models for kidney transplantation lack effective methods to evaluate clinical utility in real-world organ allocation scenarios, as traditional metrics like the C-index fail to capture the actual impact of recipient substitution on graft survival. This study leverages the SRTR database to develop a spectrum of survival models—from linear to deep learning—and introduces a novel paired-recipient evaluation framework. By counterfactually comparing outcomes when the same donor kidney is allocated to different recipients, the framework translates predictive performance into interpretable gains in post-transplant life years. Experiments show that all five models achieve approximately 60% pairwise accuracy under this framework, highlighting the limitations of the C-index in this context and offering a more clinically meaningful paradigm for organ allocation decision-making.

Abstract

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.

Problem

• survival prediction
• kidney transplant
• donor-recipient matching
• paired evaluation
• graft survival

Innovation

• paired recipient-based evaluation
• survival prediction
• deceased donor kidney transplant
• counterfactual evaluation
• C-index limitation