View clinical trials related to CVD.
Filter by:Cardiovascular disease (CVD) is one of the prominent diseases that affect many people. One cost-effective solution is to identify people at higher risk of CVD by CVD risk prediction model. China-PAR, TRS-2P, and SMART2 are common risk prediction models for prevention. However, these risk scores were mostly based on the routinely self-check health information and multivariable regression without time-varying consideration. Investigators developed a Machine Learning (ML) based risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC) among a predominantly Chinese population in Hong Kong to estimates the 10 years of secondary recurrent CVD risk for the high-risk individuals. The study objective is to evaluate the accuracy of the P-CARDIAC performance in practice among a large-scale Hong Kong population in medicine specialist outpatient clinic (SOPC) and cardiac clinic. The results will reassure cardiologists that the P-CARDIAC risk score is sensitive to the heart disease symptoms. Investigators anticipate that the results may help to facilitate P-CARDIAC in clinical setting and provide more practical information with the development of P-CARDIAC.