Clinical trial NCT06452173: Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis
Official title: Deep Learning-based Risk Prediction Model for Cerebrovascular Events in Patients With Carotid Artery Stenosis
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Carotid Stenosis
Keywords: carotid stenosis
stroke
Risk prediction model
Deep-learning
Lead sponsor: Beijing Tiantan Hospital
Sponsor class: OTHER
Collaborators: name: Beijing Neurosurgical Institute
First posted: 2024-06-11
Enrollment: 300
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients who met the enrollment criteria at Beijing Tiantan Hospital.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Number of Patients with Cerebrovascular Events
timeFrame: Within six months from the date of consultation
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Beijing Tiantan hospital
state: Beijing
Study officials:
affiliation: Beijing Neurosurgical Institution
name: Yan Zhang, MD
role: PRINCIPAL_INVESTIGATOR
References: