A donor-recipient ranking model to optimize long-term survival post liver transplant.

Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal donor-recipient matching remain major challenges and existing donor-recipient matching risk models offer limited predictive accuracy. Our study aims to develop a machine-learning-based model to predict and rank long-term post-transplant survival. We included adult deceased donor liver transplant recipients from the Scientific Registry of Transplant Recipients (2009-2019). We developed a first of its kind XGBoost-based survival model to predict graft survival at important discrete timepoints and built a corresponding ranking score. The reliability of the score was compared to existing scores. We included 60,649 waitlist registrants, of which 41,058 (67.7%) were males. We found that our predictive model, DisCScore, was the strongest at predicting donor-recipient pair survival times, with a C-index of 0.858 (95%CI: 0.8525-0.8643). We achieved the highest mean time-dependent AUROC at 6-months, 1- and 3- years post-LT using DisCScore (mean AUC (95% CI): 0.797 (0.783-0.812), 0.821 (0.809-0.832) and 0.865 (0.857-0.874) respectively and DisCScore outperformed CoxPH and DeepSurv models in all three timepoints. We found that our model performed best in the high MELD group (MELD > 30) - where the C-index was the highest: 0.873 (95% CI 0.863-0.891), as compared to the low and medium MELD groups. Our proposed model can accurately predict donor-recipient pair survival probabilities, especially in those recipients with high MELD scores. This has the potential to be integrated into the complex decision-making pathway of organ allocation.