Plasma: Plasma levels of metabolites and some proteins will be further determined
carotid high-resolution magnetic resonance imaging: Define plaque composition and morphological characteristics
Atherosclerotic carotid artery stenosis is a major cause of stroke, and early identification of high-risk patients combined with surgical intervention can significantly reduce stroke risk. Currently, stroke risk assessment in patients with carotid artery stenosis primarily relies on imaging indicators such as plaque morphology, composition, and degree of stenosis, with less emphasis on indicators directly related to inflammation, hemodynamics, and plaque instability. Certain circulating metabolites are closely linked to plaque progression and are direct risk factors for stroke. However, there is a lack of stroke risk prediction models for patients with carotid stenosis that incorporate these indicators, and the ability to identify high-risk patients needs improvement.
This study proposes using deep learning technology to integrate multidimensional data from plaque imaging, fluid dynamics, circulating metabolomics, and proteomics to construct an accurate prediction model for cerebrovascular events in patients with carotid artery stenosis. Additionally, it aims to explore markers of plaque instability characteristics based on plaque pathology. The study is expected to provide a basis for identifying high-risk patients with carotid artery stenosis, thereby laying the foundation for reducing stroke risk and improving long-term patient outcomes.
| Facility | City | Region | Status |
|---|---|---|---|
| Beijing Tiantan hospital | Beijing | Beijing Municipality |
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT06452173 on ClinicalTrials.gov ↗ ← All trials in China