LAUSR

BACKGROUND Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and determine covariate interaction using recipient and donor data, we generated a survival tree algorithm (ReSOLT) using UNOS transplant data. STUDY DESIGN The UNOS database was queried for liver transplants in patients ≥18 years old between 2000-2021. Pre-operative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival <7 days was excluded. Data were split into training and testing sets and further validated with ten-fold cross validation. Survival tree pruning and model selection was achieved based on AIC and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated. RESULTS A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (AUC = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient Hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p<0.001 among all by log rank test) with five-year and ten-year survival probabilities. CONCLUSIONS Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.