AI-assisted CT for Risk Stratification in Coronary Artery Disease (ACTION)

NCT07577609 | Recruiting

• 1 other identifier: C.A. 2792
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to learn if AI-assisted cardiac CT imaging can improve cardiovascular risk stratification and prediction of future coronary events in an adult population undergoing clinically indicated cardiac CT. The main questions it aims to answer are:

• Can AI-enhanced cardiac CT accurately assess cardiovascular risk in a real-world adult population?
• How do CT-derived plaque characteristics correlate with clinical, biochemical, and lifestyle risk factors? Researchers will compare subgroups (e.g., patients with different risk profiles, biomarkers, or imaging findings, and a subset undergoing OCT imaging) to see if differences in imaging and clinical parameters are associated with cardiovascular risk and plaque vulnerability. Participants will:
• Provide informed consent and medical history/demographic information
• Undergo blood sampling for cardiovascular and metabolic biomarkers
• Have a resting ECG performed
• Complete a detailed lifestyle and health questionnaire
• Receive a non-invasive cardiac CT scan interpreted by an expert
• Potentially receive heart rate-lowering medication (e.g., metoprolol) if required for imaging quality
• Be referred for further clinical evaluation if clinically indicated 

Conditions

Coronary Artery Disease (CAD); Computed Tomography Angiography; Biomarkers; AI (Artificial Intelligence); Lipoprotein(a); Atheroscleroses, Coronary; Myocardial Ischemia

Keywords

AI-assisted cardiac CT; Coronary CT angiography (CCTA); Cardiovascular risk stratification; Coronary plaque characterization; High-risk plaque; Machine learning; CT-FFR (fractional flow reserve); Coronary calcium scoring

Outcome Measures

Primary Outcomes (1)

• Cardiovascular risk stratification using AI-assisted cardiac CT

  Assessment of cardiovascular risk based on AI-enhanced cardiac CT imaging, including coronary artery calcium scoring, plaque characterization, and integrated risk prediction using clinical and imaging data.

  Baseline

Secondary Outcomes (6)

• Correlation between CT-derived plaque characteristics and haematological biomarkers

  Baseline

• Concordance between coronary CT angiography (CCTA) and optical coherence tomography (OCT)

  Baseline (subset undergoing OCT)

• Association between lifestyle factors and imaging-derived plaque risk features

  Baseline

• Subgroup analyses of cardiovascular risk by demographic and clinical factors

  Baseline

• Longitudinal changes in plaque composition in patients receiving statin therapy

  Baseline, 1 year, and annually up to 5 years

Other Outcomes (1)

• Development of an anonymized multimodal dataset for AI training and validation

  through study completion, up to 10 years

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All-comers adult population undergoing clinically indicated cardiac CT in routine clinical care

You may qualify if:

• Adults aged ≥18 years
• Undergoing clinically indicated cardiac CT
• Able and willing to provide informed consent

You may not qualify if:

• History of malignancy
• Abnormal renal function (e.g., eGFR or serum creatinine outside reference range)
• Contraindications to iodinated contrast agents or CT imaging

Sponsors & Collaborators

• University of Galway: lead
• Science Foundation Ireland: collaborator

Study Sites (1)

Clinical Research Facility

Galway, Ireland

RECRUITING

MeSH Terms

Conditions

Coronary Artery Disease; Myocardial Ischemia

Condition Hierarchy (Ancestors)

Coronary Disease; Heart Diseases; Cardiovascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases

Study Officials

• Faisal Sharif, MBBS, PhD, FRCPI, FESC, FACC

  University of Galway

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Faisal Sharif

CONTACT

+353 91524222; faisal.sharif@universityofgalway.ie

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: April 28, 2026
• First Posted: May 11, 2026
• Study Start: December 21, 2022
• Primary Completion (Estimated): December 21, 2032
• Study Completion (Estimated): December 21, 2032
• Last Updated: May 11, 2026

Record last verified: 2026-05

Locations

IE (1)