Big Data

For Better Hearts



With the ultimate aim to improve patient outcomes and reduce the societal burden of atrial fibrillation (AF), acute coronary syndrome (ACS) and heart failure (HF) in Europe and globally, the BigData@Heart consortium developed a data-driven translational research platform of unparalleled scale and phenotypic resolution. In BigData@Heart the following specific objectives were addressed:

To deliver clinically relevant disease phenotypes underpinning research and innovation:
  • (Re-)cast and standardise disease definitions of HF, AF and ACS and their outcomes, such that they can be operationalised in diverse clinical practice, the context of multi-morbidity, innovative clinical trial design and data settings (are computable and ‘universal’);
  • Disseminate new definitions in consensus documents together with regulatory agencies (EMA, NICE, and possibly FDA), patient representatives, healthcare providers, payers and legal/ethical experts;
  • Discover new phenotypes, develop reliable sub-phenotyping and inform new taxonomies of HF based on better understanding of underlying disease processes.

To deliver scalable insights from real world evidence, clinical- and pharmaco-epidemiology:
  • Estimate the comparative burden of HF, AF and ACS, both over time and across Europe;
  • Quantify predictors and determinants of these conditions and their progression and outcomes;
  • Quantify geographical and other sources of variation in treatments and drivers of treatment effects (including adherence).

To deliver through advanced analytics insights driving drug development and personalised medicine:
  • Deploy “omics” approaches to disease dissection and drug target validation;
  • Capture disease dynamics and continuously updated risk prediction and patient stratification – derived from (big) clinical, imaging and –omics data, to create a knowledge base for personalised treatment decisions.