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

Artificial Intelligence-assisted CT for Risk Stratification in COronary Artery Disease to PreveNt Future Coronary Events and Improve Outcomes

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 

Study Overview

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

Detailed Description

The ACTION Registry (Artificial Intelligence-assisted CT for Risk Stratification in Coronary Artery Disease) is a prospective, single-centre, observational patient registry conducted at the Clinical Research Facility, University Hospital Galway.

This registry is designed to systematically collect and integrate multimodal data from adults undergoing clinically indicated cardiac computed tomography (CT) to support advanced cardiovascular risk assessment using artificial intelligence (AI)-based approaches.

Registry Design and Procedures Eligible participants are consecutively enrolled at the time of referral for clinically indicated cardiac CT. Following informed consent, data are collected during a single baseline visit and supplemented by routine clinical data and follow-up information where available.

Registry procedures include:

• Collection of demographic and clinical data from medical records
• Blood sampling for cardiovascular and metabolic biomarkers
• Resting electrocardiogram (ECG)
• Structured questionnaires capturing lifestyle, cardiovascular history, and risk factors
• Standard-of-care cardiac CT imaging acquisition and expert interpretation
• Recording of medication use and clinically relevant interventions A subset of participants undergoing invasive imaging (e.g., optical coherence tomography) will have additional data collected for cross-modality comparison.

Data Collection and Registry Variables

The registry captures structured data across the following domains:

• Clinical and demographic characteristics
• Laboratory biomarkers
• Imaging-derived parameters from cardiac CT
• Lifestyle and behavioural factors
• Treatment and medication data

All variables are defined in a standardized data dictionary, which specifies:

• Variable definitions and formats
• Source of data (e.g., imaging system, laboratory system, questionnaire)
• Coding standards where applicable
• Acceptable ranges and units Data Management and Quality Assurance All data are anonymised using unique participant identifiers and stored in secure, access-controlled electronic systems compliant with GDPR and institutional data governance policies.

A comprehensive quality assurance plan is implemented, including:

Data Validation and Entry Controls

• Electronic data capture systems incorporate predefined validation rules
• Range checks and logical consistency checks are applied at data entry
• Automated queries are generated for missing, inconsistent, or out-of-range values Data Monitoring and Auditing
• Periodic internal data reviews are conducted to ensure completeness and accuracy
• Selected variables undergo source data verification against original records (e.g., imaging reports, laboratory systems, case report forms)
• On-site or remote monitoring may be conducted in accordance with institutional policies Source Data Verification
• A predefined proportion of records will be cross-checked with source documents
• Discrepancies will be documented, reviewed, and resolved בהתאם standard procedures Standard Operating Procedures (SOPs)

Registry operations are governed by standardized procedures covering:

• Participant recruitment and consent
• Data collection and entry
• Imaging acquisition and reporting
• Laboratory data handling
• Data management and security
• Statistical analysis and reporting
• Adverse event identification and reporting (if applicable)
• Change management and protocol amendments Sample Size Considerations As an observational registry, the study aims to recruit a representative, real-world population of adults undergoing cardiac CT. The sample size is determined pragmatically based on recruitment feasibility and expected patient volume at the study site.

The anticipated sample size is sufficient to support:

• Multivariable modelling for risk prediction
• Subgroup analyses
• Development and validation of AI-based models Handling of Missing Data

Missing or incomplete data may arise due to non-response, unavailable records, or technical limitations. The registry implements the following approach:

• Real-time prompts to minimise missing data at entry
• Documentation of reasons for missingness where available
• Statistical handling using appropriate methods (e.g., multiple imputation, sensitivity analyses), depending on the extent and pattern of missing data Statistical Analysis Plan Analyses will be conducted in accordance with a predefined statistical analysis plan.

Approaches may include:

• Descriptive statistics for baseline characteristics
• Correlation analyses between imaging, clinical, and biomarker variables
• Regression modelling for risk prediction and association analyses
• Subgroup analyses across demographic and clinical strata
• Cross-modality comparisons for imaging validation
• Longitudinal analyses where follow-up data are available Model performance may be evaluated using appropriate metrics (e.g., discrimination, calibration, and reclassification measures).

Data Use and Future Applications

The registry is designed to support:

• Clinical research and hypothesis generation
• Development and validation of AI-based cardiovascular risk models
• Integration of imaging and clinical data for precision medicine approaches An anonymised dataset will be maintained for future research, subject to governance and ethical approvals.

Ethical and Regulatory Considerations The registry is conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Ethical approval will be obtained from the appropriate research ethics committee, and all participants will provide written informed consent prior to inclusion

• Study Type: Observational
• Enrollment (Estimated): 10000

Contacts and Locations

• Study Contact: Name: Faisal Sharif; Phone Number: +353 91524222; Email: faisal.sharif@universityofgalway.ie

Study Locations

• Ireland
  • Galway, Ireland
    • Recruiting
    • Clinical Research Facility
    • Contact: Eileen Coen; Phone Number: 0861455568; Email: eileen.coen@universityofgalway.ie

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation between CT-derived plaque characteristics and haematological biomarkers	Association between imaging-derived plaque features and blood biomarkers (e.g., CRP, lipid profile, ApoB/ApoA-1 ratio).	Baseline
Concordance between coronary CT angiography (CCTA) and optical coherence tomography (OCT)	Agreement between CT-derived plaque characteristics and OCT findings in a subset of participants.	Baseline (subset undergoing OCT)
Association between lifestyle factors and imaging-derived plaque risk features	ssociation between lifestyle factors (e.g., smoking, diet, occupation) and imaging-derived Parameters.	Baseline
Subgroup analyses of cardiovascular risk by demographic and clinical factors	Differences in imaging and risk profiles across subgroups (age, sex, comorbidities, hormonal history).	Baseline
Longitudinal changes in plaque composition in patients receiving statin therapy	Evaluation of plaque progression or regression over time in participants on statin treatment.	Baseline, 1 year, and annually up to 5 years
Feasibility and utility of CT-derived fractional flow reserve (CT-FFR) and AI-based risk models	Assessment of the applicability and performance of CT-FFR and AI-based models in risk prediction.	Baseline

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Development of an anonymized multimodal dataset for AI training and validation	Creation of a secure dataset integrating imaging, clinical, biomarker, and lifestyle data for future AI development.	through study completion, up to 10 years

Collaborators and Investigators

• Sponsor: University of Galway
• Collaborators: Science Foundation Ireland
• Investigators: Principal Investigator: Faisal Sharif, MBBS, PhD, FRCPI, FESC, FACC, University of Galway

Study record dates

Study Major Dates

• Study Start (Actual): December 21, 2022
• Primary Completion (Estimated): December 21, 2032
• Study Completion (Estimated): December 21, 2032

Study Registration Dates

• First Submitted: April 28, 2026
• First Submitted That Met QC Criteria: May 5, 2026
• First Posted (Actual): May 11, 2026

Study Record Updates

• Last Update Posted (Actual): May 11, 2026
• Last Update Submitted That Met QC Criteria: May 5, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Machine learning; Cardiovascular risk stratification; High-risk plaque; AI-assisted cardiac CT; Coronary CT angiography (CCTA); Coronary plaque characterization; CT-FFR (fractional flow reserve); Coronary calcium scoring
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Heart Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Coronary Disease; Coronary Artery Disease; Myocardial Ischemia
• Other Study ID Numbers: C.A. 2792

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No