Logo image
Back
MELD 3.i: A Bayesian Updating to the Model for End-Stage Liver Disease
Journal article   Open access   Peer reviewed

MELD 3.i: A Bayesian Updating to the Model for End-Stage Liver Disease

Tomohiro Tanaka, Jennifer C. Lai, David Axelrod and Daniel Sewell
JHEP reports, Vol.7(10), 101525
10/2025
DOI: 10.1016/j.jhepr.2025.101525
PMCID: PMC12506478
PMID: 41070101
url
https://doi.org/10.1016/j.jhepr.2025.101525View
Published (Version of record) Open Access

Abstract

The Model for End-Stage Liver Disease (MELD) score has been central to liver transplant (LT) allocation, with iterative updates culminating in MELD 3.0. However, given temporal changes and variations in liver disease epidemiology across allocation systems that utilize MELD, continuous refinements are essential to ensure its optimal performance across diverse patient populations and transplant frameworks. This retrospective cohort study included all US adult liver transplant (LT) candidates listed between July 13, 2023 to June 30, 2024. Candidates from the first two-thirds of listing dates formed the training set, while the last third comprised the validation set. We employed a Bayesian proportional hazards model, incorporating MELD3.0 as informative priors to generate posterior distributions of the coefficients. The resulting model, MELD 3.1, represents the first iteration within the MELD 3.i framework. Model performance was assessed using concordance (C-) statistics, and reclassification analyzed patient redistribution and mortality risks across MELD tiers. The cohort included 13,764 candidates (41.1% female). MELD3.1 assigned a higher coefficient for female sex and a lower coefficient for creatinine, showed improved C-statistics for 90-day waitlist mortality in the validation set (0.7195 vs. 0.7152 for MELD3.0, p = 0.036). Reclassification analysis revealed that MELD3.1 yielded a net gain of 3.1% among decedents, entirely observed in female candidates. MELD3.1 also demonstrated net gains across different age groups and liver disease etiologies. Our findings support the MELD updating framework (MELD 3.i) as a proof-of-concept for sustainable and adaptive approach for periodically refining MELD using contemporary data and Bayesian methods. This iterative process enhances predictive accuracy, ensuring MELD remains responsive to evolving demographics and clinical practices in the US and other allocation systems. MELD 3.i, a Bayesian framework for continuously updating the MELD score using contemporary waitlist data, is introduced as a proof of concept, with MELD 3.1 as its first application. The findings demonstrate improved predictive accuracy for 90-day waitlist mortality compared to the original MELD 3.0, particularly for women. These results are important for clinicians, transplant centers, and policymakers aiming to optimize organ allocation while addressing persistent sex disparities. By enabling adaptive refinements within an existing model structure, this approach has the potential to offer a practical method to align liver transplant prioritization with evolving patient demographics and varying practices across nations and regions. [Display omitted] •MELD 3.i applies Bayesian updating to iteratively refine MELD 3.0 using new data.•MELD 3.0 priors were updated via MCMC with OPTN waitlist data from 2023–2024.•MELD 3.1 improved 90-day mortality prediction (C=0.7195 vs. 0.7152; p=0.036).•Reclassification analysis showed a net gain of 3.1% of decedents; all were female.•This first application of MELD 3.i is a proof-of-concept enabling continuous updates.
 Biostatistics Allocation Bayes Liver transplantation MELD 3.0

Details

Metrics

8 Record Views
Logo image