CCTA to Optimize Diagnostic Yield of Invasive Angiography With AI (CarDIA-AI)

Coronary Computed Tomographic Angiography to Optimize Diagnostic Yield of Invasive Angiography for Low-risk Patients Screened With Artificial Intelligence

Coronary artery disease (CAD) is a leading cause of death. The gold-standard test used to diagnose CAD is invasive coronary angiography (ICA). However, nearly half the patients who receive ICA are found to have no disease or non-significant disease. This means that while they receive a diagnosis, they do not receive any therapeutic benefit. This is concerning because ICA is expensive and it carries a risk to patients. A non-invasive diagnostic test, cardiac computed tomographic angiography (CCTA), has been shown to be as effective as ICA at diagnosing CAD in the right patient population, while being less expensive and less risky for patients. An optimal solution would involve screening to identify which patients are good candidates for CCTA vs. which should receive ICA. This screening tool could be used in a triage pathway to ensure that every patient gets the test that is best for them. The investigators have used Artificial Intelligence (AI) to develop a model for determining which patients should receive ICA vs. which should receive CCTA. The investigators have also developed a triage pathway to direct patients to the most appropriate test. The investigators now plan to evaluate the AI tool combined with the triage pathway through a clinical trial at Hamilton Health Sciences and Niagara Health. This model of care will reduce risk to patients, reduce wait times for ICA and reduce costs to the health care system.

Study Overview

• Status: Completed
• Conditions: Coronary Artery Disease
• Intervention / Treatment: Other: Usual Care; Other: Centralized triage with risk score-based screening for obstructive CAD

• Study Type: Interventional
• Enrollment (Actual): 251
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Canada
  • Ontario
    • Hamilton, Ontario, Canada, L8L 2X2
      • Hamilton General Hospital
    • Hamilton, Ontario, Canada, L8N 3Z5
      • McMaster University Medical Centre
    • St. Catharines, Ontario, Canada, L2S 0A9
      • St. Catharines Hospital

Participation Criteria

Eligibility Criteria

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

Description

Patients are eligible to participate if they: 1) are ≥18 years of age; 2) are referred for non-urgent (elective) outpatient ICA; 3) have an indication for ICA that includes 'Rule out CAD', 'Cardiomyopathy', or 'Stable CAD'; and 4) are able to provide informed consent in English. Patients fulfilling any of the following criteria will be ineligible to participate: 1) prior high-quality coronary computed tomographic angiography (CCTA) within the last 5 years; 2) atrial fibrillation; 3) known severe renal dysfunction (GFR <35); 4) planned non-coronary cardiac surgery; 5) any prior obstructive CAD, acute coronary syndrome, percutaneous coronary intervention, or coronary artery bypass graft; 6) known severe coronary artery calcification (calcium score >250); or have a body mass index (BMI) exceeding 40.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: Usual Care; Patients will proceed directly to ICA as originally referred.	Other: Usual Care; In the usual care group, patients will proceed directly to ICA following referral from community cardiology, as is the current standard of care. Research staff will screen participants in this group for significant CAD using the decision support tool; however, the tool's recommendations will not affect their care, as all patients in this group will invariably receive ICA.
Experimental: Centralized triage with risk score-based screening for obstructive CAD; Patients originally referred for ICA will be screened for obstructive CAD with a decision support tool that uses data from their referral forms. Patients will receive either CCTA or ICA based on their predicted probability of obstructive CAD.	Other: Centralized triage with risk score-based screening for obstructive CAD; Patients randomized to the intervention will have selected features of their medical history, recorded on their referral form, entered into a decision support tool by research personnel to generate a recommendation of whether they should proceed directly to ICA or whether they should receive CCTA. Patients with recommendations for ICA will proceed directly to ICA. Patients with recommendations for CCTA will be referred to CCTA. Based on the results of the CCTA, recommendations for medical management versus referral for ICA will be made.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Rate of normal/non-obstructive CAD diagnosed through ICA	The rate of normal or non-obstructive CAD diagnosed through ICA in patients referred for cardiac investigation. The rate for an arm (control vs experimental) is calculated by dividing the number of patients diagnosed with normal/non-obstructive CAD through ICA by the total patients allocated to the arm.	90 days (after randomization)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Quantitative assessment of number of angiograms avoided	Number of angiograms avoided due to CCTA bookings.	90 days (after randomization)
Deviation from management recommendations following CCTA (i.e. angiograms performed when not recommended)	Number of angiograms performed when not recommended.	90 days (after randomization)
Diagnostic yield of invasive angiography	Diagnostic yield is defined as the proportion of invasive angiograms that identify significant disease (≥70% stenosis) on a major coronary vessel (>2 mm) or >50% stenosis in the left main).	90 days (after randomization)
Sex differences in rate of normal/non-obstructive CAD diagnosed through ICA	Difference in the rate of normal/non-obstructive CAD diagnosed through ICA between males and females.	90 days (after randomization)
Site differences in rate of normal/non-obstructive CAD diagnosed through ICA	Difference in the rate of normal/non-obstructive CAD diagnosed through ICA between sites.	90 days (after randomization)
Budget impact of new strategy for risk stratification of CAD in low-risk patients	Cost of risk stratification of CAD in low risk patients.	90 days (after randomization)
Number of low-quality CCTAs	The quality will be graded on a per-patient basis using a three-class system: low quality, denoting an image in which the coronary anatomy cannot be clearly defined, requiring ICA within 90 days for clarification; suboptimal quality, denoting an image in which the coronary anatomy was equivocal for one or more non-prognostic vessels but not requiring ICA based on CCTA findings and clinical presentation; and high quality, denoting an image in which the coronary anatomy could be clearly defined.	90 days (after randomization)

Collaborators and Investigators

• Sponsor: Hamilton Health Sciences Corporation
• Collaborators: Population Health Research Institute; Hamilton Academic Health Sciences Organization
• Investigators: Principal Investigator: Jon-David Schwalm, MD, MSc, Hamilton Health Sciences Corporation; Principal Investigator: Jeremy Petch, PhD, Hamilton Health Sciences Corporation; Principal Investigator: Natalia Pinilla-Echeverri, MD, PhD, Niagara Health

Publications and helpful links

General Publications

• Schwalm JD, Di S, Sheth T, Natarajan MK, O'Brien E, McCready T, Petch J. A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms. Cardiovasc Digit Health J. 2021 Dec 24;3(1):21-30. doi: 10.1016/j.cvdhj.2021.12.001. eCollection 2022 Feb.
• Schwalm JD, Bouck Z, Natarajan MK, Pinilla N, Walker D, Syed N, Landry D, Sabri A, Tandon V, Nkurunziza J, Taljaard M, Sheth T. Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography. CJC Open. 2022 Nov 19;5(2):148-157. doi: 10.1016/j.cjco.2022.10.009. eCollection 2023 Feb.
• Schwalm JD, Sheth T, Pinilla-Echeverri N, Petch J. Using Artificial Intelligence to Optimize the Use of Cardiac Investigations in Patients With Suspected Coronary Artery Disease. J Soc Cardiovasc Angiogr Interv. 2024 Mar 26;3(3Part B):101305. doi: 10.1016/j.jscai.2024.101305. eCollection 2024 Mar. No abstract available.
• Petch J, Tabja Bortesi JP, Sheth T, Natarajan M, Pinilla-Echeverri N, Di S, Bangdiwala SI, Mosleh K, Ibrahim O, Bainey KR, Dobranowski J, Becerra MP, Sonier K, Schwalm JD. Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial. JMIR Res Protoc. 2025 May 21;14:e71726. doi: 10.2196/71726.

Study record dates

Study Major Dates

• Study Start (Actual): January 9, 2025
• Primary Completion (Actual): January 1, 2026
• Study Completion (Actual): January 1, 2026

Study Registration Dates

• First Submitted: October 16, 2024
• First Submitted That Met QC Criteria: October 17, 2024
• First Posted (Actual): October 18, 2024

Study Record Updates

• Last Update Posted (Actual): May 11, 2026
• Last Update Submitted That Met QC Criteria: May 8, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Coronary Angiography; Coronary Computed Tomography Angiography; Artificial Intelligence (AI)
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Heart Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Coronary Disease; Myocardial Ischemia; Coronary Artery Disease
• Other Study ID Numbers: 17103

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No