Risk Evaluation by COronary CTA and Artificial intelliGence Based fuNctIonal analyZing tEchniques - I

NCT ID: NCT05884008

Last Updated: 2024-06-07

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-05-01

Study Completion Date

2025-12-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

This study is a multicenter, retrospective imaging study. The study intends to retrospectively enroll patients with acute myocardial infarction who had received coronary CTA in a certain time-window before this event. All coronary CTA will be analyzed by anatomic, functional and radiomic analysis, assisted by artificial intelligence. The purpose of this study is to establish a coronary artery disease risk stratification system by coronary CTA.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Coronary angiography has been the gold standard for the diagnosis of coronary heart disease and PCI decision-making. However, the value of CAG in risk stratification is limited due to its invasive nature and lack of ability to evaluate coronary physiology and plaque characteristics, which often leads to over-treatment or under-treatment. In recent years, with the development and improvement of imaging technology, the resolution and diagnostic accuracy of coronary artery CTA have been greatly improved, and the subsequent anatomy and function (non-invasive CT-FFR, etc.) have made the assessment of coronary artery lesion risk multi-dimensional. Comprehensive and accurate coronary artery CTA scan plays a positive role in establishing the appropriate standard for PCI and improving the prognosis of patients. However, the existing problems of coronary artery CTA are insufficient imaging studies, complex image analysis, inconsistent diagnostic criteria, and insufficient clinical evidence. This study is one of the series of clinical studies on the topic of "Risk Evaluation by COronary Computed Tomography and Artificial Intelligence Based fuNctIonal analyZing tEchniques (RECOGNIZE)". The purpose of the study is to establish a coronary artery disease risk stratification system by coronary CTA and anatomic, functional and radiomic analysis, assisted by artificial intelligence.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Coronary Artery Disease

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Age between 18-80 years old
* Received coronary CT angiography 3 months to 5 years prior to acute coronary myocardial infarction. CCTA identified coronary atherosclerotic plaques with diameter stenosis ≥ 10%

Exclusion Criteria

* Familial hypercholesterolemia
* Prior history of percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG)
* Prior history of myocardial infarction before the recent event
* Severe liver or renal insufficiency
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Ruijin Hospital

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

RUIYAN ZHANG

Director of Cardiology Department, Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Ruiyan Zhang, M.D., Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Ruijin Hospital

Lin Lu, M.D., Ph.D.

Role: STUDY_CHAIR

Ruijin Hospital

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Cangzhou Center Hospital

Cangzhou, Hebei, China

Site Status RECRUITING

First affiliated hospital of Harbin Medical University

Harbin, Heilongjiang, China

Site Status RECRUITING

First affiliated hospital of Zhengzhou University

Zhengzhou, Henan, China

Site Status RECRUITING

Union Hospital, Tongji Medical College, Huazhong University of Science and Techonology

Wuhan, Hubei, China

Site Status RECRUITING

First Hospital of Nanjing

Nanjing, Jiangsu, China

Site Status RECRUITING

First affiliated hospital of Dalian Medical College

Dalian, Liaoning, China

Site Status RECRUITING

General Hospital of Northern Theater Command

Shenyang, Liaoning, China

Site Status RECRUITING

Ruijin Hospital, Shanghai Jiaotong University School of Medicine

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

Xinhua Hospital, Shanghai Jiaotong University School of Medicine

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

China

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Xiaoqun Wang, M.D., Ph.D.

Role: CONTACT

+86 13651839760

Shuo Feng, M.D., Ph.D.

Role: CONTACT

+86 15921388296

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

Run Guo, M.D.

Role: primary

Lei Liu, M.D.

Role: primary

Yingying Zheng, M.D.

Role: primary

Tian Xie, M.D.

Role: primary

Fei Ye, M.D.

Role: primary

Xiaolei Yang, M.D.

Role: primary

Geng Wang, M.D.

Role: primary

Jian Li, BS

Role: primary

0086 021 64370045

Yachen Zhang, M.D.

Role: primary

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

2022YFC2533502-1

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.

Deep Learning CAD Screening on Chest CT
NCT07181512 NOT_YET_RECRUITING
Biomarkers and Cardiac CT
NCT02381301 RECRUITING