ArticleJournal of hepatocellular carcinoma2026
Optimizing Hepatocellular Carcinoma Selection for Liver Transplantation: The Impact of an Integrated MELD-ALBI Score on Post-Transplant Outcomes.
Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and Aim: Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality in Egypt. The model for end-stage liver disease (MELD) score is the standard for prioritizing liver transplantation (LT) candidates but often underestimates hepatic biosynthetic failure in patients with compensated cirrhosis. This study evaluated the utility of an integrated "MELD-ALBI" classification to refine risk stratification in patients undergoing living donor liver transplantation (LDLT). Patients and Methods: This retrospective study included 76 adult patients with HCC who underwent LDLT between 2010 and 2021. Patients with early postoperative mortality (<1 month) were excluded. An integrated model was developed stratifying patients into three classes: Class 1 (low risk): ALBI grade 1, or ALBI grade 2 with MELD <12; Class 2 (intermediate risk): ALBI grade 2 with MELD ≥ 12; and Class 3 (high risk): ALBI grade 3. Results: The integrated model achieved a numerically higher diagnostic performance for predicting 1-year mortality (AUC=0.625) compared to MELD (AUC=0.611) or ALBI (AUC=0.593). Kaplan-Meier analysis showed significantly superior survival for Class 1 compared to Class 2 (P=0.029). In univariate Cox regression, Class 2 was associated with a threefold increase in mortality risk compared to Class 1 (HR 3.22, 95% CI 1.05-9.90, P=0.041), whereas survival outcomes for Class 2 and Class 3 were statistically comparable (P=0.27). In multivariate analysis adjusted for age, Class 2 maintained a high hazard ratio (HR 2.59), though statistical significance was attenuated (P=0.101). Conclusion: The integrated MELD-ALBI classification identifies a "hidden high-risk" subgroup (Class 2) underserved by standard allocation. We propose assigning exception "Z-points" to these patients to align their priority with biological urgency. This framework offers a potential model for refining allocation.
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