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Refining the Liver Donor Risk Index with Machine Perfusion: A Bayesian Approach
Journal article   Open access   Peer reviewed

Refining the Liver Donor Risk Index with Machine Perfusion: A Bayesian Approach

Tomohiro Tanaka and Daniel Sewell
Clinical and translational gastroenterology, Vol.17(1), e00921
01/2026
DOI: 10.14309/ctg.0000000000000921
PMCID: PMC12818854
PMID: 41020512
url
https://doi.org/10.14309/ctg.0000000000000921View
Published (Version of record) Open Access

Abstract

Background: The Donor Risk Index (DRI) is a widely used liver transplant allograft risk model but does not account for the increasing adoption of Machine Perfusion (MP). Methods: Using Bayesian updating, we incorporated MP into the DRI framework (DRI-MP). A Bayesian proportional hazards model with informative priors derived from the original DRI was applied to OPTN data from January 2022 to June 2024. Model performance was assessed using Harrell’s Concordance (C)-statistic, calibration plots, and Brier scores. Results: DRI-MP, defined as DRI × 0.7 for MP cases, improved 90-day graft survival discrimination (Harrell’s C-statistic: = 0.546 vs. 0.535, p = 0.040), while maintaining robust calibration. Conclusion: The Bayesian-updated DRI-MP modestly improves donor risk discrimination, reflecting contemporary transplant practice and providing an implementable tool with continuity from the original DRI.
Donor Risk Index Bayesian Bayes statistics OPTN liver transplant

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