Descrizione del progetto
Moderne soluzioni basate sull’intelligenza artificiale per l’allocazione degli organi nel trapianto di fegato
Il trapianto di fegato, un’ancora di salvezza per i pazienti affetti da cirrosi scompensata e carcinoma epatocellulare, si trova dinanzi a una sfida di cruciale importanza. Gli attuali modelli utilizzati per valutare la gravità della malattia epatica e guidare l’allocazione degli organi, e in particolare il modello per la malattia epatica in fase terminale, concepito venti anni fa, non consentono di prevedere con precisione la mortalità, soprattutto a causa della crescente percentuale di candidati al trapianto di fegato dovuto a carcinoma epatocellulare. In questo contesto, il progetto LEOPARD, finanziato dall’UE, che riunisce organizzazioni di condivisione di organi, esperti, statistici e laboratori, si avvale di algoritmi all’avanguardia basati sull’intelligenza artificiale al fine di rivoluzionare l’allocazione degli organi, offrendo una precisa stratificazione del rischio e calcolatori per decisioni complesse e integrando firme predittive avanzate. Il progetto non solo affronta un problema urgente, ma proietta l’Europa in una posizione di prima linea nell’innovazione dei programmi di assegnazione d’organi.
Obiettivo
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-HLTH-2022-TOOL-12-two-stage
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinatore
75012 Paris
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