Cardiovascular diseases – improved prediction, prevention, diagnosis, and monitoring
Summary
IHI JU is based on the idea that interdisciplinary and cross-sector collaboration will enable perspective and breakthrough innovations in healthcare, including the pharmaceutical industry but also new fields such as biopharmaceuticals, medical technologies and biotechnologies.
Programme Name
Innovative Health Initiative
Programme Description
IHI JU is based on the idea that interdisciplinary and cross-sector collaboration will enable perspective and breakthrough innovations in healthcare, including the pharmaceutical industry but also new fields such as biopharmaceuticals, medical technologies and biotechnologies.
Cardiovascular diseases – improved prediction, prevention, diagnosis, and monitoring
Detailed Call Description
To fulfil this aim, the selected project should:
Increase our understanding of the initial hallmarks of disease, which will allow for a better identification of individuals at risk for ASCVD and HF at a young age, and the creation of a clinical risk profile based on a multi-omic approach (e.g. genetic markers, transcriptomics, proteomics, and in depth multimodality imaging data) in adolescents who have either genetic and/or enrichment of specific endpoint associated risk factors (obesity, chronic kidney disease, type 1 diabetes, type 2 diabetes, genetic preponderance for HF and increased atherosclerosis).
Generate and validate a risk model better than currently used risk engines such as SCORE, by evaluating whether and to which extent risk factors identified in large prospective CVD primary prevention cohorts are predictive in a secondary prevention setting. The data from surrogate markers such as imaging, electronic health records (EHR), and predictive markers (plasma based multi- omics), as well as data from wearables, will generate a more refined risk engine.
Outline the extent to which social, ethical, and regulatory implications can be considered and quantified in the new risk models and gauge the potential additive value of data generated by wearable devices in current healthcare systems. Outline the extent to which regional and legal issues have an impact, and what models and methodologies can be used to examine this. Moreover, as the risk-benefit of wearable derived data will be ascertained in individuals who are likely to be frontrunners in the adoption (i.e. people with type 1 diabetes and people with a (genetic) risk for premature atherosclerosis and/or HF), the project should include behavioural elements to be analysed to provide suggestions to increase adoption in other populations.
Model short- and long-term economic and public health morbidity and mortality benefit/risk assessments of therapeutic intervention in people at risk with the new risk models to prevent or delay onset of CVDs.
Develop a decision tool that will allow a physician to select the intervention to best address ASCVD and HF in an individual patient. The tool will provide a risk-benefit profile, helping the physician and the patient in a decision-making process, integrating also patient reported outcome and experience measure (PROMs and PREMs) data.
Explore possibilities for novel methods of clinical development and trial execution. Based on learnings about risk prediction and pathophysiological modelling, novel surrogate endpoints may be considered for a risk-based cardiovascular outcome trial approach. The project should pave the way to transform the rather static phase 3 clinical trial approach into a more agile (more inclusive/enriched patient population, faster, cost-effective etc.) and sustainable part of clinical development.
Programme Category
EU Competitive Programmes
Total Budget
€135,000,000
Thematic Categories
Health
Research, Technological Development and Innovation
