Predicting Long-Term Allograft Survival in Liver Transplant Recipients
<|reference_start|>Predicting Long-Term Allograft Survival in Liver Transplant Recipients: Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Providing an accurate and interpretable model for individualized risk estimation of graft failure is essential for improving post-transplant care. To this end, we introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models. Using longitudinal patient follow-up data from the United States (U.S.), we develop our models on 82,959 liver transplant recipients and conduct multi-site evaluations on 11 regions. Additionally, by testing on a separate non-U.S. cohort, we explore the out-of-distribution generalization performance of various models without additional fine-tuning, a crucial property for clinical deployment. We find that the most complex models are also the ones most vulnerable to distribution shifts despite achieving the best in-distribution performance. Our findings not only provide a strong risk score for predicting long-term graft failure but also suggest that the routine machine learning pipeline with only in-distribution held-out validation could create harmful consequences for patients at deployment.<|reference_end|>
@article{gao2024predicting,
    title={Predicting Long-Term Allograft Survival in Liver Transplant Recipients},
    author={Xiang Gao, Michael Cooper, Maryam Naghibzadeh, Amirhossein Azhie,
  Mamatha Bhat, Rahul G. Krishnan},
    journal={arXiv preprint arXiv:2408.05437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05437},
    primaryClass={cs.LG}
}