DynaMELD: A Dynamic Model of End-Stage Liver Disease for Equitable Prioritization

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease (ESLD). However, 12-20% of patients listed for LT will die on the waitlist. Modern risk scores used for transplant prioritization cannot encompass the full statistical heterogeneity of patients awaiting LT, disadvantaging women and patients with cholestatic liver disease. Our study objective was to implement more equitable LT prioritization via a more expressive class of statistical models to individualize risk prediction. To do so, we created DynaMELD, a deep machine learning-based model of waitlist prioritization. DynaMELD leverages a neural network to model complex interactions between covariates, and leverages the rate-of-change (velocity) of time-varying laboratory biomarkers to predict a more personalized risk of mortality or dropout. Our study cohort comprised 53,046 patients with ESLD listed for LT from 2016- 2023 from the U.S. Scientific Registry of Transplant Recipients. Using 90-day concordance to measure risk discrimination, DynaMELD achieves 90-day concordance 0.5% higher than MELD 3.0 (p < 0.001). Using pooled group concordance (PGCI) as a measure of fairness, DynaMELD achieves a PGCI 1.2% higher for female patients (p < 0.001), 8.3% higher for patients with primary biliary cholangitis (p < 0.001), 7.2% higher for patients with primary sclerosing cholangitis (p < 0.001), and 1.5% higher for patients with acute-on-chronic liver failure Grade 1 (p < 0.001) compared to MELD 3.0. DynaMELD reclassifies members of these sub-groups into higher risk tiers, suggesting it would improve their access to organ offers. Introspecting upon DynaMELD using the method of SHapley Additive exPlanations (SHAP) values provides an individualized degree of model interpretability. Overall, DynaMELD may provide more accurate, individualized predictions of waitlist mortality or dropout to reduce inequities and fairly prioritize patients for liver transplant.