ArticleRisk management and healthcare policy2021
It's Time to Add Electrocardiography and Echocardiography to CVD Risk Prediction Models: Results from a Prospective Cohort Study.
Article in Risk management and healthcare policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Machine learning increases the prediction of stroke for Chinese hypertensive patients.Frontiers in microbiology · 2026Article
- Association Between ECG Findings and Atherosclerotic Cardiovascular Disease (ASCVD) Risk Score: A Cross-Sectional Analysis of a Cohort Study.Health science reports · 2025Article
- Utility of multimodal longitudinal imaging data for dynamic prediction of cardiovascular and renal disease: the CARDIA study.Frontiers in radiology · 2024Article
- Interventricular Septum Thickness for the Prediction of Coronary Heart Disease and Myocardial Infarction in Hypertension Population: A Prospective Study.Journal of clinical medicine · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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
settingWe used a prospective cohort in the Northeast China Rural Cardiovascular Health Study (NCRCHS).
participantsA 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).
resultsFor 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.
conclusionQT interval from electrocardiography and aortic root diameter and ventricular septal thickness from echocardiography should be included in the CVD risk prediction models.
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