Predicting Long-Term Allograft Survival in Liver Transplant Recipients
This provides an interpretable risk prediction tool for liver transplant recipients to improve post-transplant care, though it is incremental as it builds on existing survival modeling approaches.
The study tackled the problem of predicting long-term liver allograft failure, which affects about 20% of recipients within five years, by introducing the Model for Allograft Survival (MAS), a simple linear risk score that outperformed advanced survival models in evaluations across 11 U.S. regions and showed vulnerabilities in complex models to distribution shifts.
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.