Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis

Carotid Stenosis Clinical Trial

Official title:

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

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06452173
• Other study ID #: KY2024-070-02
• Secondary ID: 2024-2-1072
• Status: Enrolling by invitation
• Start date: April 15, 2024
• Est. completion date: December 31, 2026

Study information

• Verified date: June 2024
• Source: Beijing Tiantan Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 300
• Est. completion date: December 31, 2026
• Est. primary completion date: October 31, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Patients diagnosed with atherosclerotic carotid stenosis;

• Those who signed informed consent and volunteered to participate in this study.

Exclusion Criteria:

• Patients with combined severe stenosis of intracranial arteries;

• Patients with combined cardiogenic embolism, vasculitis, entrapment aneurysm and other related cerebral infarction;

• Combination of tumors, rheumatic immune system diseases, hematologic diseases and other diseases that change blood metabolism and proteomic characteristics;

• There are contraindications to magnetic resonance scanning and allergy to gadolinium contrast agent;

• Inability to cooperate in completing the relevant examinations.

Related Conditions & MeSH terms

• Carotid Stenosis
• Constriction, Pathologic

Intervention

Diagnostic Test:
• Plasma
Plasma levels of metabolites and some proteins will be further determined
• carotid high-resolution magnetic resonance imaging
Define plaque composition and morphological characteristics

Locations

Country	Name	City	State
China	Beijing Tiantan hospital	Beijing	Beijing

Sponsors (2)

Lead Sponsor	Collaborator
Beijing Tiantan Hospital	Beijing Neurosurgical Institute

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Number of Patients with Cerebrovascular Events	Patients will be categorized as having a cerebrovascular event if they meet one of the following indicators: i. cerebral infarction in the middle cerebral artery territory on the side of carotid stenosis; ii. transient ischemic attack symptoms (including motor and sensory) in the hemisphere on the side of stenosis; and iii. transient amaurosis on the side of stenosis.	Within six months from the date of consultation