Project description
Modern AI-based solutions for organ allocation in liver transplantation
Liver transplantation, a lifeline for decompensated cirrhosis and hepatocellular carcinoma (HCC) patients, faces a critical hurdle. Current models used to assess the severity of liver disease and drive organ allocation, notably the 20-year-old model for end-stage liver disease (MELD), fail to accurately predict mortality, particularly for the rising percentage of HCC liver transplantation candidates. In this context, the EU-funded LEOPARD project brings together organ sharing organisations, experts, statisticians, and labs. Through cutting-edge AI algorithms, it aims to revolutionise organ allocation, offering precise risk stratification and calculators for complex decisions, and integrating advanced predictive signatures. This project not only addresses a pressing issue but propels Europe to the forefront of innovative organ offering schemes.
Objective
Liver transplantation (LT) is a life-saving procedure for decompensated cirrhosis (DC) and hepato-cellular carcinoma (HCC). Its efficacy is hampered by the risk of death/drop-out on the Wait List (WL). This risk is driven by organ shortage and is mitigated by organ offering schemes. According to a sickest first policy, offering schemes prioritize LT candidates with the highest risk of dying, as assessed by predictive models. To drive allocation, Organ Sharing Organizations (OSOs) use a 20-year-old model, the MELD, predicting mortality in DC but not in HCC. Because of a dramatic increase in % of HCC candidates (40% against 10% in early 20ties), MELD schemes are increasingly inaccurate, with persisting 15 to 30% mortality in countries with low/medium donation rate. This scenario, together with advances in prognosis in DC and HCC candidates and statistics, prompts LT community to look for up-dated algorithms to refine offering schemes. To address this issue, key European LT stakeholders including OSOs, experts in LT, Statisticians, Research Labs and SME joined LEOPARD. Building on an innovative, harmonized OSOs pre-LT dataset and advances in modeling, LEOPARD propose to design and validate 1) an AI-based LEOPARD predictive algorithm outperforming current allocation models by better stratifying patients on the risk of mortality, to be proposed OSOs to drive allocation; 2) DC & HCC LEOPARD calculators available for professional for assistance in complex decision-making processes; 3) OMICs/radiomics predictive signatures integrated in a prototype 3rd-generation exploratory model. We expect to generate computational tools improving candidates’ outcomes, with more patients transplanted on time. Adoption of these tools should result in harmonization of European heterogeneous prioritization schemes, and in a signification reduction in disparities of access to LT, a major objective pointed out by EC. LEOPARD should place Europe in leading position for organ offering schemes.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
75012 Paris
France