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Review
. 2018 Apr 21;39(16):1481-1495.
doi: 10.1093/eurheartj/ehx487.

Big Data From Electronic Health Records for Early and Late Translational Cardiovascular Research: Challenges and Potential

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Free PMC article
Review

Big Data From Electronic Health Records for Early and Late Translational Cardiovascular Research: Challenges and Potential

Harry Hemingway et al. Eur Heart J. .
Free PMC article

Abstract

Aims: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research.

Methods and results: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health.

Conclusion: High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.

Figures

Figure 1
Figure 1
Scale (N people), phenotypic and omic resolution of population-based, hospital-based and disease-based exemplar data resources relevant to cardiovascular disease research (for further details see Boxes and ). AF, atrial fibrillation; AFGen, AF Consortium; CHD, coronary heart disease; ESC, European Society of Cardiology; EPIC, European Prospective Investigation into Cancer and Nutrition; ERFC, Emerging Risk Factors Collaboration; eMERGE, Electronic Medical Records and Genomics; HF, heart failure; PMI, precision medicine initiative; MVP, Million Veterans Programme; NICOR, National Institute for Cardiovascular Outcomes Research; NIHR, National Institute for Health Research; RPGEH, Research Programme on Genes, Environment, and Health; UCLEB, University College, London School of Hygiene and Tropical Medicine, Edinburgh, Bristol.
Figure 2
Figure 2
Electronic health record (EHR) Phenome Wide association studies (PheWAS). Source: Denny et al. (reproduced by kind permission). Each point represents the –log10(P) of a single SNP-phenotype association tested with PheWAS. This study is restricted to SNP-phenotype associations that achieved genome-wide significance (P ≤ 5 × 10−8) in at least one prior genome wide association study (GWAS) study that included individuals of European ancestry. Numbers in parentheses beside each phenotype represent the sample size within the PheWAS data set. The vertical blue line represents P = 0.05. Binary traits refer to all adequately powered, binary traits in the NHGRI Catalog with exact matches to a PheWAS phenotype. For example, 5/5 catalog SNPs associated with rheumatoid arthritis were replicated at P < 0.05 in PheWAS, and 9/15 SNPs associated with type 2 diabetes were replicated. Continuous traits are those numerically defined traits in the NHGRI Catalog that are related to PheWAS diseases (e.g. ‘iron deficiency anaemia’ was the PheWAS trait paired with the ‘serum iron level’ catalog trait).
Figure 3
Figure 3
Resolution across a range of risk factor levels (systolic and diastolic blood pressure) and range of different initial presentations of cardiovascular disease (abdominal aortic aneurysm and heart failure only shown here): discovery of heterogeneous associations in a cohort of >1m adults initially free from diagnosed cardiovascular disease using national structured linked electronic health records from the CALIBER resource, in which EHR phenotyping algorithms are created, validated and shared using a robust methodology.,

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