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This study investigates survival prediction models for deceased donor kidney transplants, utilizing a paired recipient-based evaluation framework to assess graft outcomes between two recipients of the same donor. By employing five different models, from linear to deep learning, the research achieves approximately 60% accuracy in predicting post-transplant years gained, providing a more clinically relevant metric than the traditional concordance index. The findings suggest that improved donor-recipient matching could enhance transplant outcomes, addressing a critical gap in current evaluation methods.
A novel paired recipient-based evaluation reveals that survival prediction models can accurately forecast post-transplant years gained, challenging traditional metrics in kidney transplant outcomes.
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.
