Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases

Completed

• Conditions: Coronary Artery Disease
• Interventions: Other: No intervention

• Registration Number: NCT04941560
• Lead Sponsor: China National Center for Cardiovascular Diseases

Brief Summary

The purposes of this study are 1) to explore the association between multi-dimension facial characteristics and the increased risk of coronary artery diseases (CAD); 2) to evaluate the diagnostic efficacy of multi-dimension appearance factors for coronary artery diseases.

Detailed Description

Previous study demonstrated the feasibility of using deep learning to detect coronary artery disease based on facial photos. However, several limitations made the algorithm hard to be utilized in clinical practice, including low specificity and lack of external validation. Adding multi-dimension facial characteristics may further increase the algorithm effect.

Thus, the investigators designed a single-center, cross-sectional study to explore the association between multi-dimension facial characteristics and CAD and to evaluate the predictive efficacy of multi-dimension appearance factors for CAD. The investigators will recruit patients undergoing coronary angiography or coronary computer tomography angiography. Patients' baseline information and multi-dimension facial images will be collected. The investigators will train and validate a deep learning algorithm based on multi-dimension facial photos.

Study Timeline

• Study First Submitted: 2021-06-22
• Study First Submitted QC: 2021-06-22
• Study First Posted: 2021-06-28
• Study Start: 2021-09-06
• Primary Completion: 2023-02-10
• Study Completion: 2023-02-10
• Status Verified: 2023-03
• Last Update Submitted: 2023-03-17
• Last Update Posted: 2023-03-21

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 460

Inclusion Criteria

• Undergoing coronary angiography or coronary computer tomography angiography
• Written informed consent

Exclusion Criteria

• Prior percutaneous coronary intervention (PCI)
• Prior coronary artery bypass graft (CABG)
• Screening coronary artery disease before treating other heart diseases
• Without blood biochemistry outcome
• With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)
• Other situations which make patients fail to be photographed

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Algorithm training and test group	No intervention	Patients undergoing coronary angiography or coronary computer tomography angiography will be enrolled. Patients data will be used to training and validate the algorithm for CAD detection based on facial photos.

Primary Outcome Measures

Name	Time	Method
Area under receiver operating curve (AUC)	At the end of enrollment (1 mouth)	Area under receiver operating curve of algorithm assessed in test group

Secondary Outcome Measures

Name	Time	Method
Sensitivity of algorithm	At the end of enrollment (1 mouth)	Sensitivity of algorithm assessed in test group
Specificity of algorithm	At the end of enrollment (1 mouth)	Specificity of algorithm assessed in test group
Positive predictive value (PPV)	At the end of enrollment (1 mouth)	PPV of algorithm assessed in test group
Negative predictive value (NPV)	At the end of enrollment (1 mouth)	NPV of algorithm assessed in test group
Diagnostic accuracy rate	At the end of enrollment (1 mouth)	Diagnostic accuracy rate of algorithm assessed in test group

Trial Locations

Locations (1)

Fuwai hospital; 🇨🇳 Beijing, Beijing, China