Liver Retransplantation: Identifying Factors and Developing a Risk Prediction Model to Predict Futility in the Modern Era

Background. Liver retransplantation (rLT) results have traditionally been inferior compared with those of primary liver transplantation. Understanding the risks and anticipated outcomes is essential for patient counseling and obtaining informed consent. Methods. Using the Scientific Registry of Transplant Recipients database, we analyzed a large cohort of adult rLT cases in the contemporary era to identify variables associated with posttransplant outcomes (with a focus on 1- and 5-y survival). Model predictions were made with random survival forests, a machine learning approach integrated into survival analysis. The difference in the out-of-bag C-index between a model with and without the variable was used to define variable importance. A prospective holdout cohort was used to validate the model predictions. Results. Of the 3774 patients studied, the overall adjusted 1- and 5-y patient survival rates increased from 76.3% and 63.0%, respectively, for those transplanted in 2010, to 81.1% and 67.4% in 2019, then decreased to 78.0% and 63.9%, respectively, in 2022. The most important predictors of posttransplant mortality include recipient characteristics (being on life support before transplant, number of previous liver transplants, age, body mass index, and Karnofsky score) and donor organ characteristics (cold ischemia time and donor age). In a prospective validation cohort stratified into risk tertiles, the high-risk group had significantly lower 1-y survival (63.7%) compared with medium-risk (83.2%) and low-risk (88.7%, P < 0.001) groups. We developed a user-friendly online application using recipient and donor characteristics to predict 1- and 5-y survival. Conclusions. The study model could be used as an additional tool to predict 1- and 5-y patient survival to help counsel prospective rLT candidates and guide donor selection in this technically challenging recipient group.