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.

The authors have declared no competing interest.

This study was funded by the Canadian Institutes of Health Research.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study used the Scientific Registry of Transplant Recipients' (SRTR) registry, maintained by the Organ Procurement and Transplantation Network (OPTN) in the United States. Data access to this registry for bona fide research or analyses in the field of organ transplantation can be requested from the SRTR. (https://www.srtr.org/about-the-data/the-srtr-database/)

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The code required to reproduce the results of this manuscript is linked in the article. The data underlying the present study can be requested from the Scientific Registry of Transplant Recipients. (https://www.srtr.org/about-the-data/the-srtr-database/)