Clinical trial NCT05856110: Risk Evaluation by COronary CTA and Artificial IntelliGence Based fuNctIonal analyZing tEchniques - II
Official title: Coronary Computed Tomography Angiography and Functional Analysis in Risk Stratification of Coronary Artery Disease: A Prospective, Multi-center Cohort Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Artery Disease
Keywords: Coronary CTA
CT-FFR
Risk Stratification
Artificial Intelligence
Lead sponsor: Ruijin Hospital
Sponsor class: OTHER
First posted: 2023-05-12
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This study is a multi-center cohort study. Patients with clinical angina pectoris and suspected coronary artery disease from nine clinical centers across P.R. China, including outpatient and hospitalized patients, are consecutively enrolled.

Brief summary: This study is a multicenter, prospective clinical cohort study. The study intends to continuously enroll patients with coronary heart disease. All subjects will undergo coronary CTA (followed by anatomic, functional and radiomics analysis), proteomics research as well as clinical follow-up of cardiovascular events. The purpose of this study is to establish a new, non-invasive cardiovascular disease risk stratification system.

Detailed description: 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 evaluate the accuracy of early identification of cardiovascular high-risk groups based on the functional evaluation model based on coronary CTA images through a multi-center, prospective clinical cohort study, so as to establish an early, non-invasive and accurate risk classification of cardiovascular events in coronary heart disease.

Eligibility criteria: Inclusion Criteria:

* Age 18-80 years
* Clinically significant angina pectoris, suspecting coronary artery disease

Exclusion Criteria:

* Unsuitable for coronary CTA (such as severe renal impairment, uncontrolled thyroid condition, allergic to iodine, etc.)
* Without a visible plaque (defined as ≥ 30% stenosis) in major coronary arteries in coronary CTA
* Prior history of myocardial infarction or heart failure
* Prior history of percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG)
* Severe renal or liver failure
* Familial hypercholesterolemia
* Estimated survival ≤1 year
* Malignant tumor
* Poor coordinance, unable to follow-up

Primary outcomes:
measure: Major Adverse Cardiovascular Events
timeFrame: 2 years
Secondary outcomes:
measure: Cardiovascular death
timeFrame: 2 years
measure: Myocardial infarction
timeFrame: 2 years
measure: Stroke
timeFrame: 2 years
measure: Coronary Revascularization
timeFrame: 2 years
Other outcomes:
Locations:
city: Cangzhou
country: China
facility: Cangzhou Center Hospital
state: Hebei
city: Harbin
country: China
facility: First affiliated hospital of Harbin Medical University
state: Heilongjiang
city: Zhengzhou
country: China
facility: First affiliated hospital of Zhengzhou University
state: Henan
city: Wuhan
country: China
facility: Union Hospital, Tongji Medical College, Huazhong University of Science and Techonology
state: Hubei
city: Nanjing
country: China
facility: First Hospital of Nanjing
state: Jiangsu
city: Dalian
country: China
facility: First affiliated hospital of Dalian Medical College
state: Liaoning
city: Shenyang
country: China
facility: General Hospital of Northern Theater Command
state: Liaoning
city: Shanghai
country: China
facility: Xinhua Hospital, Shanghai Jiaotong University School of Medicine
state: Shanghai
city: Shanghai
country: China
facility: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Study officials:
affiliation: Ruijin Hospital
name: Ruiyan Zhang, M.D., Ph.D.
role: PRINCIPAL_INVESTIGATOR
affiliation: Ruijin Hospital
name: Lin Lu, M.D.
role: STUDY_CHAIR
affiliation: First Hospital of Nanjing
name: Fei Ye, M.D.
role: STUDY_CHAIR
affiliation: Xinhua Hospital, Shanghai Jiaotong University School of Medicine
name: Yachen Zhang, M.D.
role: STUDY_CHAIR
affiliation: Wuhan Union Hospital, China
name: Tian Xie, M.D.
role: STUDY_CHAIR
affiliation: First Affiliated Hospital of Harbin Medical University
name: Lei Liu, M.D.
role: STUDY_CHAIR
affiliation: The First Affiliated Hospital of Dalian Medical University
name: Xiaolei Yang, M.D.
role: STUDY_CHAIR
affiliation: Cangzhou Center Hospital
name: Run Guo, M.D.
role: STUDY_CHAIR
affiliation: The First Affiliated Hospital of Zhengzhou University
name: Yingying Guo, M.D.
role: STUDY_CHAIR
affiliation: The General Hospital of Northern Theater Command
name: Geng Wang, M.D.
role: STUDY_CHAIR
References: