Clinical trial NCT06181552: Development of CV Risk Prediction Tools Based on AI and Fundus Imaging Technology Study (PERFECT)
Official title: Development of Cardiovascular Risk Prediction Tools Based on Artificial Intelligence and Fundus Imaging Technology Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cardiovascular Diseases
Keywords: cardiovascular risk prediction
fundus imaging
Lead sponsor: China National Center for Cardiovascular Diseases
Sponsor class: OTHER_GOV
Collaborators: name: Beijing Tongren Hospital
First posted: 2023-12-26
Enrollment: 1072
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants with CVD, or otherwise with high CVD risk will be enrolled.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Diagnosis of ASCVD at baseline
timeFrame: At enrollment
measure: Major cardiovascular events
timeFrame: during the 1 year follow-up
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: National Center for Cardiovascular Diseases, Fuwai Hospital
name: Jing Li, PhD, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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. Epub 2018 Feb 19.