Risk Management and Healthcare Policy (Nov 2021)

It’s Time to Add Electrocardiography and Echocardiography to CVD Risk Prediction Models: Results from a Prospective Cohort Study

  • Li Z,
  • Yang Y,
  • Zheng L,
  • Sun G,
  • Guo X,
  • Sun Y

Journal volume & issue
Vol. Volume 14
pp. 4657 – 4671

Abstract

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Zhao Li,1 Yiqing Yang,1 Liqiang Zheng,2 Guozhe Sun,1 Xiaofan Guo,1 Yingxian Sun1 1Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, People’s Republic of China; 2Department of Clinical Epidemiology, Library, Department of Health Policy and Hospital Management, Shengjing Hospital of China Medical University, Shenyang, 110004, People’s Republic of ChinaCorrespondence: Yiqing Yang; Yingxian Sun Tel +86 24 83282688Fax +86 24 83282346Email [email protected]; [email protected]: To develop and validate a new prediction model for the general population based on a large panel of both traditional and novel factors in cardiovascular disease (CVD).Design and Setting: We used a prospective cohort in the Northeast China Rural Cardiovascular Health Study (NCRCHS).Participants: A total of 11,956 participants aged ≥ 35 years were recruited between 2012 and 2013, using a multistage, randomly stratified, cluster-sampling scheme. In 2015 and 2017, the participants were invited to join the follow-up study for incident cardiovascular events. The loss to follow-up number was 351. At the study’s end, we obtained the CVD outcome events for 10,349 participants.Primary and Secondary Outcome Measures: The prediction model was developed using demographic factors, blood biochemical indicators, electrocardiographic (ECG) characteristics, and echocardiography indicators collected at baseline (Model 1). Framingham-related variables, namely age, sex, smoking, total and high-density lipoprotein cholesterol and diabetes status were used to construct the traditional model (Model 2).Results: For the observed population (n = 10,349), the median follow-up time was 4.66 years. The total incidence of CVD was 1.1%/year, including stroke (n = 342) and coronary heart disease (n = 175). The results of Model 1 indicated that in addition to the traditional risk factors, QT interval (p < 0.001), aortic root diameter (p < 0.001), and ventricular septal thickness (p < 0.001) were predictive factors for CVD. Decision curve analysis (DCA) showed that the net benefit with Model 1 was higher than that of Model 2.Conclusion: QT interval from electrocardiography and aortic root diameter and ventricular septal thickness from echocardiography should be included in the CVD risk prediction models.Keywords: CVD, predictive model, general cohort, QT interval, aortic root diameter, ventricular septal thickness

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