Ignite Creation Date:
2024-06-16 @ 11:52 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06452173
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-06-13
First Post:
2024-06-05
Brief Title:
Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis
Sponsor:
Beijing Tiantan Hospital
Organization:
Beijing Tiantan Hospital
Study Overview
Official Title:
Deep Learning-based Risk Prediction Model for Cerebrovascular Events in Patients With Carotid Artery Stenosis
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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
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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 2024-2-1072 | OTHER | Capitals Funds for Health Improvement and Research | None |