Explainable machine learning for prognostic modeling of waitlist mortality in cirrhotic liver transplantation

Accurate mortality prediction in liver transplant (LT) candidates is essential for improving organ allocation and prioritization. Models like Model for End-Stage Liver Disease (MELD) are widely used, but may overlook complex nonlinear interactions between risk factors. Machine learning (ML) offers improved predictive accuracy but often at the expense of interpretability. In this study, we conduct a comprehensive comparison of three MELD-based scores against advanced ML models, including LDA, TabNet, RF and LightGBM to predict 3-, 6-, and 12-month waitlist mortality, using retrospective data from the UNOS/OPTN registry. SHapley Additive exPlanations (SHAP) were exploited to provide deeper insights into the best model's decision-making process, offering both global and local explanations while pinpointing key risk factors. LightGBM emerged as the best-performing model achieving AUROC of 0.921, 0.892, and 0.872 for 3-, 6-, and 12-month mortality predictions, respectively. Moreover, our proposed Ensemble Learning Transplant Mortality (ELTM) score, derived from LightGBM, not only enhanced overall risk assessment but also improved equity and patient prioritization. The explanation component highlighted key predictors beyond traditional MELD components, such as patient's functional state, age at registration, degree of ascites, and bilirubin changes over time. By introducing an explainable ML framework for prognostic modeling, this study provides a transparent data-driven approach that could enhance the efficiency and fairness of organ allocation, potentially saving lives by prioritizing patients more accurately.