Predicting Long-Term Allograft Survival in Liver Transplant Recipients

• URL: http://arxiv.org/abs/2408.05437v1
• Date: Sat, 10 Aug 2024 04:44:36 GMT
• Title: Predicting Long-Term Allograft Survival in Liver Transplant Recipients
• Authors: Xiang Gao, Michael Cooper, Maryam Naghibzadeh, Amirhossein Azhie, Mamatha Bhat, Rahul G. Krishnan,
• Abstract summary: Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant. We introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models.
• Score: 11.680219281917076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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