NCT06452173

Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis

Deep Learning-based Risk Prediction Model for Cerebrovascular Events in Patients With Carotid Artery Stenosis

• Status: Enrolling By Invitation
• Phase: —
• Study type: Observational
• Enrollment: 300 (estimated)
• Sponsor: Beijing Tiantan Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

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.

Conditions

• Carotid Stenosis

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Plasma	Plasma levels of metabolites and some proteins will be further determined
DIAGNOSTIC_TEST	carotid high-resolution magnetic resonance imaging	Define plaque composition and morphological characteristics

Timeline

• Start date: 2024-04-15
• Primary completion: 2026-10-31
• Completion: 2026-12-31
• First posted: 2024-06-11
• Last updated: 2024-06-13

Locations

1 site across 1 country: China