Yeo Khung Keong, Ong Hean-Yee, Chua Terrance, Lim Zheng Jie, Yap Jonathan, Ho Hee Hwa, Jaufeerally Fazlur, Tong Khim-Leng, Kojodjojo Pipin, Wong Hwee-Bee, Heng Derrick, Tan Kelvin Bryan, Richards Arthur Mark, Teoh Kristine Leok-Kheng, Sin Kenny, Tan Ngiap Chuan, Lee Simon Biing Ming, Lim Terence, Ta Andy, Liok Edwin, Lau Yee How, Gao Fei, Liman Christian, Sarkar Joydeep, Sahlén Anders, Koh Tian Hai, Chan Mark Y
Department of Cardiology, National Heart Centre Singapore Singapore.
Duke-NUS Medical School Singapore.
Circ Rep. 2019 Dec 7;2(1):33-43. doi: 10.1253/circrep.CR-19-0106.
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). 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. By integrating multiple incongruent data sources, SINGCLOUD enables in-depth analysis of real-world cardiovascular "big data".
关于冠状动脉疾病（CAD）患者临床结局和医疗质量的真实世界数据是零散的。我们描述了新加坡心血管纵向结局数据库（SingCLOUD）的基本原理和设计。我们设计了一个健康数据网格，以整合来自所有公立资助医院和基层医疗诊所的临床、行政、实验室、程序、处方和财务数据，这些机构提供了新加坡80%的医疗服务。在此，我们解释我们协调来自不同电子医疗和非医疗平台的真实世界数据以开发一个强大的纵向数据集的方法。我们展示了2012年至2014年间接受经皮冠状动脉介入治疗（PCI）的心肌梗死（MI）患者的试点数据。初始数据集有53395名患者。其中，35203名患者经冠状动脉造影确诊患有CAD，其中21521名接受了PCI。最终，限于2012 - 2014年，3819名患者患有MI并接受了PCI，而5989名患者患有MI。与拥有189个分析字段的质量改进登记处新加坡心脏数据库相比，SingCLOUD平台生成了另外313个数据字段，并且能够识别另外250例心力衰竭事件、2年内发生的664例主要不良心血管事件以及3747名患者1年内的低密度脂蛋白水平。通过整合多个不一致的数据源，SINGCLOUD能够对真实世界的心血管“大数据”进行深入分析。
