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Building a Longitudinal National Integrated Cardiovascular Database　- Lessons Learnt From SingCLOUD.

Khung Keong Yeo1,2, Hean-Yee Ong3, Terrance Chua1,2

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|March 11, 2021
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Summary
This summary is machine-generated.

Singapore

Keywords:
Big dataDatabaseHeart failureMyocardial infarction

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Area of Science:

  • Cardiovascular research
  • Health informatics
  • Real-world data analysis

Background:

  • Fragmented real-world data hinders understanding of coronary artery disease (CAD) clinical outcomes and care quality.
  • Existing data sources lack comprehensive longitudinal patient information.
  • Need for integrated data to support robust cardiovascular research.

Purpose of the Study:

  • To describe the rationale and design of the Singapore Cardiovascular Longitudinal Outcomes Database (SingCLOUD).
  • To explain the approach for harmonizing diverse real-world data into a robust, longitudinal dataset.
  • To present pilot data on myocardial infarction (MI) patients treated with percutaneous coronary intervention (PCI).

Main Methods:

  • Developed a health data grid to integrate clinical, administrative, laboratory, procedural, prescription, and financial data.
  • Harmonized data from diverse electronic medical and non-medical platforms across public healthcare institutions.
  • Utilized pilot data from 3,819 patients with MI and PCI between 2012-2014.

Main Results:

  • SingCLOUD platform generated 313 additional data fields compared to the Singapore Cardiac Data Bank.
  • Identified 250 additional heart failure events and 664 major adverse cardiovascular events at 2 years.
  • Captured 1-year low-density lipoprotein levels for 3,747 patients.

Conclusions:

  • SingCLOUD enables in-depth analysis of real-world cardiovascular 'big data' by integrating multiple data sources.
  • The platform enhances the depth and breadth of cardiovascular data analysis.
  • Facilitates improved understanding of clinical outcomes and quality of care for CAD patients.