Cardiovascular DiseasesPERFECTNCT06181552
Development of CV Risk Prediction Tools Based on AI and Fundus Imaging Technology Study (PERFECT)
An observational study of Cardiovascular Diseases, run by China National Center for Cardiovascular Diseases.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Cardiovascular Diseases
- What is being tested?
- fundus photograpgy, optical coherence tomography and optical coherence tomography angiography
- How many people
- 1,072 participants
- Where
- No sites listed yet
- Study dates
- Dec 2023to Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants with CVD, or otherwise with high CVD risk will be enrolled.
Who may be able to join
- 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.
Who cannot join
- 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
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- 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
Trial locations and contacts
No sites are listed yet.
About this study
Official 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.
Read the 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.
- Conditions
- Cardiovascular Diseases
- Being tested
- fundus photograpgy, optical coherence tomography, optical coherence tomography angiography
- Keywords
- cardiovascular risk prediction, fundus imaging
- Medical subject headings
- Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
fundus photograpgyDiagnostic test
All the participants will undergo fundus photography.
optical coherence tomographyDiagnostic test
Also called OCT
All the participants will undergo OCT examination.
optical coherence tomography angiographyDiagnostic test
Also called OCT-A
All the participants will undergo OCT-A examination.
| Study group | Role | What they receive |
|---|---|---|
| Participants with CVD | 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 Receives: Diagnostic Test: fundus photograpgy; Diagnostic Test: optical coherence tomography; Diagnostic Test: optical coherence tomography angiography | |
| Participants with high CVD risk | 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. Receives: Diagnostic Test: fundus photograpgy; Diagnostic Test: optical coherence tomography; Diagnostic Test: optical coherence tomography angiography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosis of ASCVD at baseline
Whether participants have established ASCVD at baseline
Measured: 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
Measured: during the 1 year follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Dec 2023
- Study startDec 2023 (estimated)
- Primary completionMar 2025 (estimated)
- Study completionJun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- More trials run by China National Center for Cardiovascular DiseasesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,072 participants (estimated)
Sponsor and investigators
- Sponsor type
- Other government
- Collaborators
- Beijing Tongren Hospital
- Responsible party
- Sponsor
- Investigators
- Jing Li, PhD, MD, Principal investigator, National Center for Cardiovascular Diseases, Fuwai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06181552
- Sponsor's protocol ID
- 2023-GSP-GG-10
- Acronym
- PERFECT
- Status last verified
- Dec 2023
- First submitted
- 9 Nov 2023
- Last updated
- 26 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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.
PubMed 31015713
Study at a glance
From the registry record, last updated by the sponsor on 26 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Cardiovascular Risk Prediction Tools Based on Artificial Intelligence and Fundus Imaging Technology Study
- ClinicalTrials.gov ID
- NCT06181552
- Acronym / sponsor ID
- PERFECT / 2023-GSP-GG-10
- Conditions
- Cardiovascular Diseases
- Keywords
- cardiovascular risk prediction, fundus imaging
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,072 participants (estimated)
- Lead sponsor
- China National Center for Cardiovascular DiseasesOther government
- Collaborators
- Beijing Tongren Hospital
- Study start
- Dec 2023 (estimated)
- Primary completion
- Mar 2025 (estimated)
- Study completion
- Jun 2025 (estimated)
- First posted
- 26 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Participants with CVD | – | Diagnostic Test: fundus photograpgy, Diagnostic Test: optical coherence tomography, Diagnostic Test: optical coherence tomography angiographyMeeting 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 | – | Diagnostic Test: fundus photograpgy, Diagnostic Test: optical coherence tomography, Diagnostic Test: optical coherence tomography angiographyParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants with CVD, or otherwise with high CVD risk will be enrolled.
Key inclusion criteria (8 listed)
- 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:
Key exclusion criteria (11 listed)
- Participants unable to provide fundus imaging data required for the study due to the following reasons:
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| fundus photograpgyAll the participants will undergo fundus photography. | Diagnostic test | Participants with CVD, Participants with high CVD risk |
| optical coherence tomographyAlso: OCTAll the participants will undergo OCT examination. | Diagnostic test | Participants with CVD, Participants with high CVD risk |
| optical coherence tomography angiographyAlso: OCT-AAll the participants will undergo OCT-A examination. | Diagnostic test | Participants with CVD, Participants with high CVD risk |
Primary outcome
Diagnosis of ASCVD at baseline
Time frame: At enrollment
Whether participants have established ASCVD at baseline
Major cardiovascular events
Time frame: 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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Dec 2023.
- Results publication: PubMed 31015713Poplin 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jing Li, PhD, MD principal investigator
National Center for Cardiovascular Diseases, Fuwai Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases, run by China National Center for Cardiovascular Diseases.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 26 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jing Li, PhD, MD (+86 60866077, jing.li@fwoxford.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosis of ASCVD at baseline (time frame: At enrollment). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06181552, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Dec 2023.