Deadline: 20-Sep-22
The European Commission (EC) is pleased to announce a call for proposals for the Cardiovascular Diseases – Improved Prediction, Prevention, Diagnosis, and Monitoring.
The overall aim of the project is to provide tools for the earlier diagnosis of atherosclerosis and heart failure as well as earlier identification of patients at risk. This includes biomarker or predictive algorithms to assess changes in risk and stratify patients according to individual responses to therapeutic intervention.
Currently, patient data from various sources such as devices, intake forms, and diagnostic and exploratory tests are not integrated or monitored to give a complete understanding of the patient’s disease state. Integration of these data sets, e.g. by a federated database, and its accessibility to healthcare providers and researchers will provide better understanding to help detect, monitor, and treat ASCVD and HF.
The selected project should clearly outline their approach for data capture, storage and sharing, for instance data federation, or an open, centralised database architecture.
The proposed data management strategy should be sustainable, seek synergies with other relevant projects, and align with the FAIR principles.
Funding Information
The check will normally be done for the coordinator if the requested grant amount is equal to or greater than EUR 500 000, except for:
- public bodies (entities established as a public body under national law, including local, regional or national authorities) or international organisations; and
- cases where the individual requested grant amount is not more than EUR 60 000 (lowvalue grant).
Expected Outcomes
This project is expected to achieve all of the following outcomes:
- Identification of relevant data sets, for instance derived from classical diagnostic screening; in-vitro diagnostics; ‘multi-omic’ platforms (comprising genomic, transcriptomic, proteomic and multimodality imaging data, most preferably with multiple timepoint assessments to ascertain the directionality and dynamics of relevant changes); continuous glucose monitoring (CGM) data, continuous electrocardiogram (ECG) data from wearables. In addition HF and activity data, wearable devices, digital health applications and routine clinical practice.
- Leverage data in currently available federated databases with ‘open access’ generated during, for example, IMI1/IMI2 projects in compliance with GDPR (General Data Protection Regulation), such as results/data/biomarkers/electronic health records provided by project participants, adding to the knowledge base.
- Demonstration of the utility of biomarker combinations including data from different modalities e.g., wearables, smart (acute or chronic) care setting devices, imaging/screening for the diseases and comorbidities.
- Based on existing biomarker combinations, determination of whether new biomarkers are needed for detecting patients at risk.
- Developed and/or evaluated artificial intelligence (AI) models that, using data from various sources, can identify patient subgroups who require and respond differently to the prevention and/or treatment of atherosclerotic cardiovascular disease (ASCVD) and HF in clinical practice.
- Identification of previously undiagnosed subgroups of ASCVD and HF patients, for instance people with insulin resistance, diabetes, and obesity, into clinically meaningful subgroups.
- Documentation and analysis of patient preferences regarding information, diagnosis and treatment of CVD, as well as requirements and preferences of individuals to share their data.
- Integration of patient data (e.g. via a federated database concept) to enable a holistic overview of specific patient groups to enable more effective and efficient disease management and execution of screening programmes and individual treatment tailoring.
- Inclusion of validated patient reported outcome and experience measure (PROMs and PREMs) data including biophysical, mental and psychosocial parameters with the aim of using it in a clinical setting. This may include, but is not limited to, measures on quality of life, sleep quality, physical activity, emotional stress, satisfaction with treatment, healthcare service experience.
- Leveraging developed algorithms/decision trees to define and validate care pathways that tailor therapy towards individual patient needs and compare them to the “one-size-fits-all” approach.
- Sustainability of relevant results and data repositories.
- Identification of incentives that reward positive health behaviour and motivate consistent and continuous data generation especially when health status has changed.
- Utilisation of the knowledge gained from the project to facilitate and guide better prevention, considering the patient perspective.
- Data collection in the patient population with type 1 diabetes that historically has been excluded from clinical trials. Identifying the highest-risk individuals (in the paediatric, adolescent and adult populations, among others) to aim for more intensive contemporary CVD risk lowering agents (such as glucose, lipid and blood pressure lowering), and other, ideally personalised, cardioprotective adjunct therapies could help reduce the burden of CVD and contribute to improving outcomes in type 1 diabetes.
- Data collection in patient populations with other (genetically defined) predispositions to CVD and HF, that historically have been excluded from clinical trials. Identifying the highest-risk individuals could contribute to improving the outcomes in people with obesity, type 2 diabetes or (genetic) predisposition to CVD/HF.
Eligibility Criteria
To be eligible for funding, applicants must be established in one of the eligible countries, i.e.:
- the Member States of the European Union, including their outermost regions;
- the Overseas Countries and Territories (OCTs) linked to the Member States;
-
eligible non-EU countries:
- countries associated to Horizon Europe;
- low- and middle-income countries.
For more information, visit European Commission.
For more information, visit https://bit.ly/3uazBEC
