‘BigData@Heart will show how big data can drive progress in the treatment and management of CVD’, Prof. Diederick E. (Rick) Grobbee, University Medical Center Utrecht (UMCU)
Cardiovascular disease (CVD) causes more than 3.9 million deaths each year across Europe – accounting for 45% of all deaths - with 1.3 million of these deaths occuring before the age of 75 years. Of the total cost of CVD in the EU (€210 billion a year), around 53% (€111 billion) is due to health care costs, 26% (€54 billion) to productivity losses and 21% (€45 billion) to the informal care of people with CVD. The lack of high-resolution biomarkers and computable definitions frustrates progress in the development of successful CVD therapies. Thus, there is a strong need for a better definition of CVD through improved biomarkers and endpoints, as well as its outcomes and prognoses.
The project, which was launched in March 2017, brings together key players and stakeholders in the CVD field to address these challenges. BigData@Heart is a 5-year, € 19 million project supported by the Innovative Medicines Initiative (IMI), a public-private partnership between the European Union and the European pharmaceutical industry. The BigData@Heart consortium, consisting of 19 partners coming from academia, medical associations, pharmaceutical industry, SMEs and patient organisations and led by Prof. Diederick E. (Rick) Grobbee from the University Medical Center Utrecht (UMCU) and Dr. Gunnar Brobert from Bayer, will develop a data-driven translational research platform which will be aiming at delivering clinically relevant disease phenotypes, scalable insights from real-world evidence, best-practices in drug development, and personalised medicines through advanced analytics.
This project will develop and test a framework that will enable big data driven cardiovascular research, including the development of:
- New definitions of diseases and outcomes that are universal, computable, and relevant for patients, clinicians, industry and regulators.
- Informatics platforms that link, visualise and harmonise data sources of varying types, completeness and structure.
- Data science techniques to develop new definitions of disease, identify new phenotypes, and construct personalized predictive models.
- Guidelines that allow for cross-border usage of big data sources acknowledging ethical and legal constraints as well as data security.
Better understanding of heart disease, the development of new therapy targets, improved drug and device development/utilisation, and laying a scientific foundation for progress in the personalised treatment and management of CVD are among the expected impact of BigData@Heart on science, industry, policies and patients.