Development of CV Risk Prediction Tools Based on AI and Fundus Imaging Technology Study (PERFECT) (PERFECT)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Cardiovascular Diseases
• Intervention / Treatment: Diagnostic test: fundus photograpgy; Diagnostic test: optical coherence tomography; Diagnostic test: optical coherence tomography angiography

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.

• Study Type: Observational
• Enrollment (Estimated): 1072

Contacts and Locations

• Study Contact: Name: Jing Li, PhD, MD; Phone Number: +86 60866077; Email: jing.li@fwoxford.org
• Study Contact Backup: Name: Bin Wang, PhD, MD; Phone Number: +86 60866220; Email: wangbin@fuwai.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Participants with CVD, or otherwise with high CVD risk will be enrolled.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Participants with CVD; Meeting any of the following: 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); Stroke	Diagnostic test: fundus photograpgy; All the participants will undergo fundus photography.; Diagnostic test: optical coherence tomography; All the participants will undergo OCT examination.; Other Names: OCT; Diagnostic test: optical coherence tomography angiography; All the participants will undergo OCT-A examination.; Other Names: OCT-A
Participants with high CVD risk; Participants without CVD, but 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.	Diagnostic test: fundus photograpgy; All the participants will undergo fundus photography.; Diagnostic test: optical coherence tomography; All the participants will undergo OCT examination.; Other Names: OCT; Diagnostic test: optical coherence tomography angiography; All the participants will undergo OCT-A examination.; Other Names: OCT-A

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnosis of ASCVD at baseline	Whether participants have established ASCVD at baseline	At enrollment
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

Collaborators and Investigators

• Sponsor: China National Center for Cardiovascular Diseases
• Collaborators: Beijing Tongren Hospital
• Investigators: Principal Investigator: Jing Li, PhD, MD, National Center for Cardiovascular Diseases, Fuwai Hospital

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): December 1, 2023
• Primary Completion (Estimated): March 1, 2025
• Study Completion (Estimated): June 1, 2025

Study Registration Dates

• First Submitted: November 9, 2023
• First Submitted That Met QC Criteria: December 20, 2023
• First Posted (Actual): December 26, 2023

Study Record Updates

• Last Update Posted (Actual): December 26, 2023
• Last Update Submitted That Met QC Criteria: December 20, 2023
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Keywords: fundus imaging; cardiovascular risk prediction
• Additional Relevant MeSH Terms: Cardiovascular Diseases
• Other Study ID Numbers: 2023-GSP-GG-10

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No