Heart failure (HF) is a chronic clinical condition involving up to 6.5 million people in Europe, the most frequent cause of hospitalization in adults with a 5-year mortality rate up to 70%. Several drugs positively modify the course of disease in the patients with HF with reduced ejection fraction (HFrEF), with a high level of evidence. Besides, the use of diuretics, the basic cornerstone of symptoms relief in HFrEF by targeting congestion, is supported by poor and outdated evidence. Congestion drives symptoms worsening leading to hospital admission. Clinical evaluation of congestion is often inaccurate and insensitive to detect interstitial or intravascular fluid overload, and thus insufficient to guide use of diuretics. Indeed, their use is inefficient, with studies showing that up to 70% of patients with chronic HFrEF show congestion despite diuretic therapy, the use of diuretics does prevent clinical events in patients discharged after an acute heart failure episode, and diuretics may represent a barrier to adherence to disease modifier therapies. An appropriate management of diuretic therapy is therefore crucial to overcome the risk of re-hospitalisation, manage patients symptoms, and achieve target guideline-directed medical therapy. To fill these gaps, BIOTOOL-CHF will 1) validate a set of qualified biomarkers estimating congestion, 2) define a multiparametric artificial intelligence-based score predicting congestion and prognosis, 3) develop a decision-making tool to manage congestion by diuretics, 4) develop a Point of Care companion diagnostic (CD) to assess biomarkers concentrations 5) set up a Strategy plan for industrial development and market access of the CD. This approach will support the definition of a framework to regulatory agencies and scientific societies to disseminate recommendations for a more efficient use of existing pharmaceuticals and allow a personalised strategy for the management of HFrEF, by using new tools and digital solutions.

Population: adult | Intervention type: Non-interventional

The LEOPARD project stands as a pioneering effort in the field of liver transplantation (LT), uniting stakeholders across Europe to revolutionise organ allocation strategies for individuals with decompensated cirrhosis (DC) and hepatocellular carcinoma (HCC).

Among patients currently listed for liver transplantation in Europe, mortality/drop-out on the waitlist averages 15-20% with large disparities across European countries. In recognition of the critical need to prioritise LT candidates based on mortality risk, particularly in the context of organ shortages, and the limitations of existing predictive models such as the Model for End-Stage Liver Disease (MELD) in accurately assessing this risk, there is a growing urgency for updated algorithms to refine organ offering schemes.

The LEOPARD project seeks to enhance liver transplantation outcomes by creating and validating an AI-based predictive algorithm, considering recently identified predictors, that surpasses current models in stratifying both DC and HCC patients by mortality/dropout risks on the waitlist. Additionally, the project will develop calculators for DC and HCC candidates to aid in patient prioritisation, as well as integrate predictive signatures from OMICs/radiomics to improve risk assessment accuracy.

At its core, the LEOPARD project aims to improve patient outcomes by ensuring timely transplantations, harmonising European prioritisation schemes, and advocating for equitable access to LT to significantly reduce mortality on the waitlist. 

Population: adult | Intervention type: interventional excluding health product