Risk Evaluation by Coronary Imaging and Artificial Intelligence-Based Functional Analyzing Techniques: Plasma Proteomic Profiles of Atheroma Classified by Intracoronary Imaging.

What this Trial Is About

Researchers are conducting a single-center, prospective cohort study to identify new circulating biomarkers that can help predict high-risk coronary plaques early. The study focuses on patients diagnosed with chronic coronary syndrome (CCS) or non-ST-segment elevation acute coronary syndrome (NSTE-ACS) who have marginal or obstructive lesions in major coronary arteries. These plaques will be assessed using advanced imaging techniques to better understand their characteristics and potential risks. Participants will undergo noninvasive coronary CT angiography (CCTA) or invasive coronary angiography (ICA) to detect coronary lesions. Optical coherence tomography (OCT), sometimes combined with intravascular ultrasound (IVUS) and near infrared spectroscopy (NIRS), will be used to classify plaque types and measure plaque burden. Blood samples will be collected after overnight fasting to analyze plasma proteomic profiles using liquid chromatography-mass spectrometry (LC-MS/MS) and machine learning methods. The study will follow participants every 3 months for 2 years to monitor outcomes. During the study, researchers will collect detailed imaging and blood data to link plaque features with novel biomarkers. They will track the occurrence of major adverse cardiovascular events (MACEs), cardiovascular death, myocardial infarction, and unplanned revascularization over the 2-year follow-up. This thorough monitoring aims to improve early detection of high-risk plaques and better understand their impact on heart health.

Risk Evaluation by COronary Imaging and Artificial intelliGence Based fuNctIonal analyZing tEchniques - IV