Coronary angiography has been the gold standard for the diagnosis of coronary heart disease and PCI decision-making. However, the value of CAG in risk stratification is limited due to its invasive nature and lack of ability to evaluate coronary physiology and plaque characteristics, which often leads to over-treatment or under-treatment. In recent years, with the development and improvement of imaging technology, the resolution and diagnostic accuracy of coronary artery CTA have been greatly improved, and the subsequent anatomy and function (non-invasive CT-FFR, etc.) have made the assessment of coronary artery lesion risk multi-dimensional. Comprehensive and accurate coronary artery CTA scan plays a positive role in establishing the appropriate standard for PCI and improving the prognosis of patients. However, the existing problems of coronary artery CTA are insufficient imaging studies, complex image analysis, inconsistent diagnostic criteria, and insufficient clinical evidence. This study is one of the series of clinical studies on the topic of Risk Evaluation by COronary Computed Tomography and Artificial Intelligence Based fuNctIonal analyZing tEchniques (RECOGNIZE). The purpose of the study is to evaluate the accuracy of early identification of cardiovascular high-risk groups based on the functional evaluation model based on coronary CTA images through a multi-center, prospective clinical cohort study, so as to establish an early, non-invasive and accurate risk classification of cardiovascular events in coronary heart disease.
This study is a multicenter, prospective clinical cohort study. The study intends to continuously enroll patients with coronary heart disease. All subjects will undergo coronary CTA (followed by anatomic, functional and radiomics analysis), proteomics research as well as clinical follow-up of cardiovascular events. The purpose of this study is to establish a new, non-invasive cardiovascular disease risk stratification system.