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

Coronary Computed Tomography Angiography and Functional Analysis in Risk Stratification of Coronary Artery Disease: A Retrospective Cohort Study

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05884008

Acronym

RECOGNIZE-I

Enrollment

300

Registered

2023-06-01

Start date

2023-05-01

Completion date

2025-12-31

Last updated

2024-06-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Coronary Artery Disease

Keywords

Coronary CTA, CT-FFR, Artificial Intelligence, Risk Stratification

Brief summary

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

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
Coronary artery plaque risk level	3 months to 5 years prior to acute myocardial infarction	Coronary plaque risk was determined using an artificial intelligence (AI) guided risk stratification model based on Coronary CTA structural, functional and radiomic analysis.

Countries

China

Contacts

Primary ContactXiaoqun Wang, M.D., Ph.D.

xiaoqun_wang@hotmail.com+86 13651839760

Backup ContactShuo Feng, M.D., Ph.D.

fengshuorv@hotmail.com+86 15921388296

Outcome results