Cost-effectiveness Analysis of Selection Criteria for Liver Transplantation in Patients with Hepatocellular Carcinoma

Abstract

Introduction:Liver transplantation is the most effective curative treatment for patients with hepatocellular carcinoma.Due to the scarcity of cadaveric donor livers, selection criteria have been established. However, thesecriteria are very restrictive.In this study, we analysed a new selection tool called HepatoPredict (ClassI and ClassII) and comparedit with standard criteria, including the Milan Criteria (MC), UCSF, Up-to-7, AFP Model, andMetroTicket 2.0. We conducted a cost-effectiveness analysis from the perspective of the U.S. healthcaresystem to determine which selection criterion provides the most significant benefit to the health system.Methodology:A Markov model was developed to simulate the health status of patients with hepatocellular carcinoma who underwent liver transplantation over a five-year period. Transition probabilities, costs, and QALYs were obtained from published articles. The probabilities of recurrence were computed by Kaplan-Meier estimators and were based on a cohort of 149 patients from Portugal and Spain. We analysed the mean of recurrence-free survival, life years gained, quality of life, and the incremental cost-effectiveness ratio (ICER) relative to the MC. Results: HepatoPredict offers the most benefits but has a higher total cost than the other criteria. The ICER of HepatoPredict-ClassI and HepatoPredict-ClassII relative to the MC was $15 557.32/ QALY and $40 960.19/QALY, respectively. These two values were below the cost-effectiveness threshold (U.S. GDP per capita, $81 632.25/QALY), which means that HepatoPredict is acceptable in the U.S. healthcare system. Conclusion: HepatoPredict stands out as the most cost-effective criterion and allocates organs most efficiently, considering their scarcity. This provides a significant advantage for hospitals.