Cardiovascular Diseases Clinical Trial
— PERFECTOfficial title:
Development of Cardiovascular Risk Prediction Tools Based on Artificial Intelligence and Fundus Imaging Technology Study
This study aims to develop a cardiovascular disease (CVD) screening tool and cardiovascular risk prediction tool based on fundus imaging data with the method of artificial intelligence.
Status | Not yet recruiting |
Enrollment | 1072 |
Est. completion date | June 2025 |
Est. primary completion date | March 2025 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: Three types of participants will be included, which are: - Participants with established coronary heart disease, including previously diagnosed myocardial infarction, previous treatment with coronary intervention or coronary artery bypass grafting, coronary artery stenosis =50%, or chest pain with objective evidence of myocardial ischemia (myocardial ischemia indicated by stress electrocardiogram or stress imaging) - Participants with established stroke. - Participants without coronary heart disease or stroke, but are at high risk for CVD, defined as meeting at least two of the following: 1. Men aged = 60 years old, or women aged = 65 years old; 2. Diabetes; 3. Total cholesterol>5.2 mmol/L, or LDL-C>3.4 mmol/L, or HDL-C<1.0 mmol/L; 4. Currently smoking, defined as daily smoking lasting for 1 year or more. Exclusion Criteria: - Participants unable to provide fundus imaging data required for the study due to the following reasons: 1. Permanent blindness, blurred vision, flying mosquito disease, or refractive medium opacity seriously affecting fundus examination, such as severe cataracts, vitreous hemorrhage, etc. 2. Macular edema, severe nonproliferative retinopathy in diabetes, proliferative vitreoretinopathy, radiation ophthalmopathy or retinal vein occlusion 3. Eyeball enucleation, eye deformities, etc. 4. Previous retinal laser therapy, injection therapy for any eye, or history of retinal surgery 5. Photosensitivity, or taking medication that can cause photosensitivity, or currently undergoing photodynamic therapy 6. Unable to cooperate with examination for collection of fundus imaging data 7. Other situations that the participants fail to provide fundus imaging data required for the study - Suffering from other serious diseases with an expected survival period of less than one year, such as advanced malignant tumors - Unable to adhere to follow-up - Other conditions which the researchers consider inappropriate for participants to enroll in the study |
Country | Name | City | State |
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n/a |
Lead Sponsor | Collaborator |
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China National Center for Cardiovascular Diseases | Beijing Tongren Hospital |
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018 Mar;2(3):158-164. doi: 10.1038/s41551-018-0195-0. Ep — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Diagnosis of ASCVD at baseline | Whether participants have established ASCVD at baseline | At enrollment | |
Primary | Major cardiovascular events | a composite of myocardial infarction, coronary or non coronary revascularization surgery, hospitalization or emergency treatment due to new-onset or worsening heart failure, stroke or cardiovascular death | during the 1 year follow-up |
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