Where is the perfect triangle in the liver allocation system?
Authors: Kazunari Sasaki, Marc L Melcher
Journal: The Lancet Healthy Longevity (2024)

Fair and equitable allocation of livers for transplantation should consider three components: patient urgency, organ utilisation, and survival benefit. In the UK, the liver allocation algorithm, introduced in March 2018, prioritises maximising life-years gained from liver transplantation using the transplant benefit score (TBS).1NHS Blood and TransplantNational Liver Offering Scheme.https://www.odt.nhs.uk/odt-structures-and-standards/odt-hub-programme/national-liver-offering-scheme/Date accessed: March 27, 2024Google Scholar Anothony Attia and colleagues conducted a modelling study in which they analysed data from 1808 patients over two decades using an advanced simulation to examine recipient prioritisation related to age within the TBS framework.2Attia A Webb J Connor K et al.Effect of recipient age on prioritisation for liver transplantation in the UK: a population-based modelling study.Lancet Healthy Longev. 2024; 5: e346-e355Summary Full Text Full Text PDF Google Scholar This study showed that the TBS algorithm is biased towards transplanting older patients (aged ≥60 years). Older patients were more prone to waiting list dropout due to frailty and therefore, deemed to have greater survival benefit from liver transplantation and had a higher TBS than younger patients (aged 25–49 years). Despite being below the minimum listing criteria, some older patients received organ offers due to increased prioritisation while a considerable number younger patients with severe disease (UK Model for End-Stage Liver Disease [UKELD] score >60) did not receive offers in the simulation model. By contrast, allocation systems based solely on medical urgency, exemplified by the Model for End-Stage Liver Disease [MELD] 3·0 and UKELD, showed no age bias in their simulation. However, allocation algorithms based on disease severity neglect efficient organ utilisation and survival benefit of liver transplantation for individual patients. Consequently, these systems do not optimise survival benefits conferred by the valuable societal resource constituted by donors' altruism. The debate on whether recipient age should be accounted for in the organ allocation system remains controversial. Some people argue that age discrimination in these algorithms is unfair and unethical. However, prioritising younger recipients for transplant should improve overall transplant outcomes and maximise organ utility as older transplant patients tend to have shorter survival than younger patients, primarily due to comorbidities and increased risk of losing grafts due to factors unrelated to the transplant itself.3Snyder A Kojima L Imaoka Y et al.Evaluating the outcomes of donor–recipient age differences in young adults undergoing liver transplantation.Liver Transpl. 2023; 29: 793-803Crossref Scopus (3) Google Scholar A system that prioritises survival benefits from liver transplantation is considered a more equitable approach to address age-related issues. Nevertheless, Attia and colleagues note that the current TBS-based algorithm shows a bias that should be addressed in future updates. Furthermore, they recommend adopting shorter-term survival predictions for calculating updated TBS, similar to UKELD and MELD 3.0, to provide a more immediate evaluation of patient needs and benefits of transplantation. They also highlight the limitations of the existing 5-year survival predictions, which might not accurately capture the full benefits of transplantation for younger patients, who have a longer life expectancy post-transplant. Organ allocation organisations globally continue to grapple with the challenge of devising the ideal organ allocation system that equitably balances patient urgency, organ utilisation, and survival benefits afforded by liver transplantation. Inherently, like any system, achieving perfection that satisfies all stakeholders is difficult. Data-driven simulation analysis, such as that conducted by Attia and colleagues, is a strategy for approximating the ultimate objective. However, as this study highlighted, the solution might not lie in substituting complex human decision-making processes with algorithms. Rather, fostering collaborative interactions between clinicians and algorithms could pave the way for achieving optimal outcomes, especially when resources are constrained. The trajectory of data-driven methodologies appears promising for finding improved organ allocation systems, yet they necessitate comprehensive statistical simulations with many variables, a challenging task using conventional statistical techniques. Particularly, the medical conditions and likelihood of patient dropout from the waiting list represent dynamic elements that are difficult to assess with static statistical modelling. These variables are subject to constant fluctuation and any simulation attempting to account for the evolving circumstances of recipients, alongside donor characteristics, demands extensive programming and computational efforts. Use of artificial intelligence might enable the transplant community to find novel solutions to improve donor–recipient matching algorithms.4Bhat M Rabindranath M Chara BS Simonetto DA Artificial intelligence, machine learning, and deep learning in liver transplantation.J Hepatol. 2023; 78: 1216-1233Summary Full Text Full Text PDF PubMed Scopus (0) Google Scholar Nevertheless, only a few simple studies using artificial intelligence-based organ allocation systems have been published, with most comparing machine learning or neural network models against traditional statistical approaches.5Kwong A Hameed B Syed S et al.Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma.Cancer Med. 2022; 11: 1535-1541Crossref Scopus (7) Google Scholar, 6Bertsimas D Kung J Trichakis N Wang Y Hirose R Vagefi PA Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation.Am J Transplant. 2019; 19: 1109-1118Summary Full Text Full Text PDF PubMed Scopus (69) Google Scholar The quest for an ideal allocation system that equally addresses urgency, utilisation, and survival benefits remains a formidable challenge. We declare no competing interests. Effect of recipient age on prioritisation for liver transplantation in the UK: a population-based modelling studyThe UK liver allocation algorithm prioritises older patients for transplantation by predicting that advancing age increases the benefit from liver transplantation. Restricted follow-up and biases in waiting list data might limit the accuracy of these benefit predictions. Measures beyond overall waiting list mortality are required to fully capture the benefits of liver transplantation. Full-Text PDF Open Access