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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Jeremy Petch, PhD
- Phone Number: 416-476-9039
- Email: petchj@HHSC.CA
Study Contact Backup
- Name: Jon-David Schwalm, MD, MSc
- Phone Number: 905-577-1423
- Email: JD.Schwalm@phri.ca
Study Locations
-
-
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
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
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
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Active Comparator: Usual Care
Patients will proceed directly to ICA as originally referred.
|
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.
|
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
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Rate of normal/non-obstructive CAD diagnosed through ICA
Time Frame: 90 days (after randomization)
|
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
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Quantitative assessment of number of angiograms avoided
Time Frame: 90 days (after randomization)
|
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)
Time Frame: 90 days (after randomization)
|
Number of angiograms performed when not recommended.
|
90 days (after randomization)
|
|
Diagnostic yield of invasive angiography
Time Frame: 90 days (after randomization)
|
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
Time Frame: 90 days (after randomization)
|
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
Time Frame: 90 days (after randomization)
|
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
Time Frame: 90 days (after randomization)
|
Cost of risk stratification of CAD in low risk patients.
|
90 days (after randomization)
|
|
Number of low-quality CCTAs
Time Frame: 90 days (after randomization)
|
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
Sponsor
Collaborators
Collaborators
Investigators
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)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 17103
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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