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

Abstract

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 [phone]Fax [phone]Email [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

Keywords

• cvd
• predictive model
• general cohort
• qt interval
• aortic root diameter
• ventricular septal thickness