Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records

Spiros C Denaxas1, Julie George, Emily Herrett

  • 1Department of Epidemiology and Public Health, Clinical Epidemiology, University College London, London, UK. s.denaxas@ucl.ac.uk

Insights

The Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Health Records (CALIBER) program links diverse health data. This approach generates new evidence for cardiovascular disease (CVD) policy and patient care.

Area of Science:

  • Health Informatics
  • Epidemiology
  • Public Health

Background:

  • Cardiovascular disease (CVD) research requires integrated data for policy.
  • Electronic health records (EHRs) offer new opportunities for CVD research.
  • The CALIBER program aims to leverage linked EHR data for CVD insights.

Purpose of the Study:

  • To establish a framework for linking multiple electronic health record sources.
  • To integrate bespoke investigator-led cohort studies with registry data.
  • To create a common data model for research-ready EHR data.

Main Methods:

  • Linking primary care data (CPRD), acute coronary syndrome registries (MINAP), hospitalization data (HES), and mortality data (ONS).
  • Integrating large-scale cohort studies (e.g., UK Biobank) with disease registries.
  • Developing a common data model with over 300 curated variables for risk factors, CVDs, and comorbidities.

Main Results:

  • CALIBER links diverse EHR sources, including primary care, hospital admissions, and mortality data.
  • The program facilitates phenotyping and validation of cardiovascular diseases through data linkage.
  • A standardized data model enhances the research readiness and shareability of EHR data.

Conclusions:

  • The CALIBER program provides a robust infrastructure for CVD research.
  • Linked EHR data enable comprehensive analysis across the patient journey.
  • This approach supports evidence-based healthcare and public health policy for cardiovascular diseases.

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