Building a Longitudinal National Integrated Cardiovascular Database　― Lessons Learnt From SingCLOUD ―

Abstract

Background:Real world data on clinical outcomes and quality of care for patients with coronary artery disease (CAD) are fragmented. We describe the rationale and design of the Singapore Cardiovascular Longitudinal Outcomes Database (SingCLOUD).

Methods and Results:We designed a health data grid to integrate clinical, administrative, laboratory, procedural, prescription and financial data from all public-funded hospitals and primary care clinics, which provide 80% of health care in Singapore. Here, we explain our approach to harmonize real-world data from diverse electronic medical and non-medical platforms to develop a robust and longitudinal dataset. We present pilot data on patients with myocardial infarction (MI) treated with percutaneous coronary intervention (PCI) between 2012 and 2014. The initial data set had 53,395 patients. Of these, 35,203 had CAD confirmed on coronary angiography, of whom 21,521 had PCI. Eventually, limiting to 2012–2014, 3,819 patients had MI with PCI, while 5,989 had MI. Compared with the quality improvement registry, Singapore Cardiac Data Bank, which had 189 fields for analysis, the SingCLOUD platform generated an additional 313 additional data fields, and was able to identify an additional 250 heart failure events, 664 major adverse cardiovascular events at 2 years, and low-density lipoprotein levels to 1 year for 3,747 patients.

Conclusions:By integrating multiple incongruent data sources, SINGCLOUD enables in-depth analysis of real-world cardiovascular “big data”.