A Simple Model for Predicting 10-Year Cardiovascular Risk in Middle-Aged to Older Chinese: Guangzhou Biobank Cohort Study

Ying Yue Huang, Wen Bo Tian, Chao Qiang Jiang, Wei Sen Zhang, Feng Zhu, Ya Li Jin, Tai Hing Lam, Lin Xu, Kar Keung Cheng

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The aim of this paper is to develop 10-year cardiovascular disease (CVD) risk prediction models for the contemporary Chinese populations based on the Guangzhou Biobank Cohort Study (GBCS) and to compare its performance with models based on Framingham’s general cardiovascular risk profile and the Prediction for Atherosclerotic CVD Risk in China (China-PAR) project. Subjects were randomly classified into the training (n = 15,000) and validation (n = 12,721) sets. During an average of 12.0 years’ follow-up, 3,732 CVD events occurred. A 10-year sex-specific CVD risk prediction model including age, systolic blood pressure, use of antihypertensive medication, smoking, and diabetes was developed. Compared with the Framingham and China-PAR models, the GBCS model had a better discrimination in both women (c-statistic 0.72, 95% CI 0.71–0.73) and men (c-statistic 0.68, 95% CI 0.67–0.70), and the risk predicted was closer to the actual risk. This prediction model would be useful for identifying individuals at higher risks of CVD in contemporary Chinese populations. Graphical abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)416-426
Number of pages11
JournalJournal of Cardiovascular Translational Research
Volume15
Issue number2
DOIs
Publication statusPublished - Apr 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

ASJC Scopus Subject Areas

  • Molecular Medicine
  • Genetics
  • Pharmaceutical Science
  • Cardiology and Cardiovascular Medicine
  • Genetics(clinical)

Keywords

  • Cardiovascular disease
  • Prediction model
  • Primary prevention
  • Risk assessment

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