Scaling up innovation in cardiovascular health

Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination “Tackling diseases and reducing disease burden”. To that end, proposals under this topic should aim to deliver results that are directed at, tailored towards and contributing to all the following expected outcomes:Healthcare providers, policymakers and researchers benefit from an improved knowledge base and collaboration on the key challenges and gaps on cardiovascular health research and a conceptual framework to develop a roadmap for research and innovation is established.Health systems gain improved and standardised evidence to better prevent, diagnose or treat cardiovascular diseases (CVDs) and associated comorbidities, based on the research results on prediction, early detection, screening practices and diagnostic methods and tools, including via personalised and digital approaches.Medical and non-medical health professionals and technology developers have an increased knowledge, awareness and capacity to uptake and deliver effective and innovative approaches for risk prediction, early detection, screening and health management strategies, such as Virtual Human Twins (VHT)[1] or Artificial Intelligence (AI)-based applications. This involves supporting, strategic foresight, improving health literacy and cross-sectoral knowledge exchange and collaboration to drive innovation in personalised prevention and cardiovascular risk prediction.Healthcare providers and policymakers have an improved knowledge base to inform future strategies for early detection and prevention of CVDs, with specific attention to women and vulnerable groups, through research on personalised risk prediction approaches that consider multiple and interacting risk factors (e.g. genetic predisposition, environmental pollutants, diet, lifestyle habits, multimorbidity, sex and gender). Scope:CVDs are the main cause of death in the EU, with over 1.7 million de