Development of CV Risk Prediction Tools Based on AI and Fundus Imaging Technology Study (PERFECT)
- Conditions
- Cardiovascular Diseases
- Interventions
- Diagnostic Test: fundus photograpgyDiagnostic Test: optical coherence tomographyDiagnostic Test: optical coherence tomography angiography
- Registration Number
- NCT06181552
- Lead Sponsor
- China National Center for Cardiovascular Diseases
- Brief Summary
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.
- Detailed Description
This study will establish a cohort of individuals including patients with CVD and participants with high CVD risk, and all the study participants will be follow-up for 1 year. By collecting baseline clinical data, fundus imaging data, and CVD events during the follow up, this study aims to distinguish CVD status based on the fundus imaging data, and explore the association between fundus imaging data and occurence of CVD during the follow up. By using machine learning approach, this study aims to construct a CVD screening tool and CVD prediction tool based on fundus imaging data.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 1072
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:
- Men aged ≥ 60 years old, or women aged ≥ 65 years old;
- Diabetes;
- Total cholesterol>5.2 mmol/L, or LDL-C>3.4 mmol/L, or HDL-C<1.0 mmol/L;
- Currently smoking, defined as daily smoking lasting for 1 year or more.
-
Participants unable to provide fundus imaging data required for the study due to the following reasons:
- Permanent blindness, blurred vision, flying mosquito disease, or refractive medium opacity seriously affecting fundus examination, such as severe cataracts, vitreous hemorrhage, etc.
- Macular edema, severe nonproliferative retinopathy in diabetes, proliferative vitreoretinopathy, radiation ophthalmopathy or retinal vein occlusion
- Eyeball enucleation, eye deformities, etc.
- Previous retinal laser therapy, injection therapy for any eye, or history of retinal surgery
- Photosensitivity, or taking medication that can cause photosensitivity, or currently undergoing photodynamic therapy
- Unable to cooperate with examination for collection of fundus imaging data
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Participants with high CVD risk fundus photograpgy Participants without CVD, but 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. Participants with high CVD risk optical coherence tomography Participants without CVD, but 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. Participants with CVD optical coherence tomography Meeting any of the following: 1. 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 (indicated by stress electrocardiogram or stress imaging) 2. Stroke Participants with CVD optical coherence tomography angiography Meeting any of the following: 1. 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 (indicated by stress electrocardiogram or stress imaging) 2. Stroke Participants with CVD fundus photograpgy Meeting any of the following: 1. 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 (indicated by stress electrocardiogram or stress imaging) 2. Stroke Participants with high CVD risk optical coherence tomography angiography Participants without CVD, but 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.
- Primary Outcome Measures
Name Time Method Diagnosis of ASCVD at baseline At enrollment Whether participants have established ASCVD at baseline
Major cardiovascular events during the 1 year follow-up 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
- Secondary Outcome Measures
Name Time Method