ExpectedOutcome:The results of the selected project will provide the basis for better primary and secondary prevention of CVD. The goal is to identify existing comprehensive CVD and heart failure (HF) patient datasets (with contextual parameters e.g., behavioural, socioeconomic, gender, ethnicity) and integrate them with data from diagnostic tools (e.g. wearables, imaging devices, bio samples / biopsies) and routine clinical practice. This will provide the basis for independently validated prediction models for improving the stratification of patients, and reveal insights to achieve earlier intervention. Additionally, the project will leverage developed algorithms to define and validate care pathways that tailor therapy towards individual patient needs and compare it to the “one-size-fits-all” approach.

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 dir... ver más

ExpectedOutcome:The results of the selected project will provide the basis for better primary and secondary prevention of CVD. The goal is to identify existing comprehensive CVD and heart failure (HF) patient datasets (with contextual parameters e.g., behavioural, socioeconomic, gender, ethnicity) and integrate them with data from diagnostic tools (e.g. wearables, imaging devices, bio samples / biopsies) and routine clinical practice. This will provide the basis for independently validated prediction models for improving the stratification of patients, and reveal insights to achieve earlier intervention. Additionally, the project will leverage developed algorithms to define and validate care pathways that tailor therapy towards individual patient needs and compare it to the “one-size-fits-all” approach.

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.
Scope: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 principles1. 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 generated from this topic could provide an exploratory and interactive platform to discuss the validity of novel methods of evidence generation, such as the use of data from wearable devices. 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. Specifically, the project should engage in the Regulatory Science Research Needs initiative, launched by the European Medicines Agency (EMA), assessing the utility of real-world healthcare data to improve the quality of randomised controlled trial simulations (H2.3.3). During the COVID-19 pandemic, the world has experienced a transition to virtual and remote care as more and more patients connect with their health care teams online. This presents an enormous opportunity and benefits for patients. A pathway forward could be to through use of real-world evidence (RWE) data to address sex, ethnicity and race disparities in cardiovascular outcome trials and better promote CV management. 1 http://www.go-fair.org/fair-principles/


Expected Impact:Cardiovascular disease (CVD) remains one of the leading causes of death globally and, as such, has a major impact at a personal, societal, and economic scale. Over 60 million people in the EU live with CVDs, at an economic cost of EUR 210 billion annually.

CVD risk assessment is not fully implemented in many clinical practices across Europe1 and treatment of CVD is commonly practiced as with a “one size fits all” approach, meaning that all patients are treated with a standard medical regimen regardless of risk level. The prevalence of well-established CVD risk factors such as obesity, diabetes, and chronic kidney disease is rising, and combined with an ageing population in Europe, the urgency for a more personalised approach to cardiovascular risk assessment becomes evident.

The generation of a personalised risk-benefit approach, based on data derived from transcriptomic, proteomic and multimodality imaging studies, combined with data from electronic interventions via CE certified wearable devices, such as smartwatches or activity trackers, as well as routine clinical data from medical devices, will contribute to all of the following impacts:

Accuracy of diagnosis and efficacy of treatment will increase thanks to an individualised sub-phenotype-risk approach which will allow for risk-focused targeted therapy.Patients will be empowered and encouraged to take control over their health by accessing an integrated overview, including biometric data derived from wearables, of their health information, which can also be used for a more informed dialogue with their healthcare provider(s). Early diagnosis of CVDs, combined with better understanding of the mechanisms involved, will lead to the development of more cost-effective strategies, and the identification of new care pathways. 1 Rossello X, Dorresteijn JA, Janssen A, Lambrinou E, Scherrenberg M, Bonnefoy-Cudraz E, Cobain M, Piepoli MF, Visseren FL, Dendale P, This Paper Is A Co-Publication Between European Journal Of Preventive Cardiology European Heart Journal Acute Cardiovascular Care And European Journal Of Cardiovascular Nursing. Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). Eur J Prev Cardiol. 2019 Sep;26(14):1534-1544. doi: 10.1177/2047487319846715. Epub 2019 Jun 24. PMID: 31234648.


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