Clinical trial NCT06648239: CCTA to Optimize Diagnostic Yield of Invasive Angiography With AI
Official title: Coronary Computed Tomographic Angiography to Optimize Diagnostic Yield of Invasive Angiography for Low-risk Patients Screened With Artificial Intelligence
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Coronary Artery Disease
Keywords: Artificial Intelligence (AI)
Coronary Angiography
Coronary Computed Tomography Angiography
Lead sponsor: Hamilton Health Sciences Corporation
Sponsor class: OTHER
Collaborators: name: Population Health Research Institute
name: Hamilton Academic Health Sciences Organization
First posted: 2024-10-18
Enrollment: 252
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

Brief summary: 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.


Eligibility criteria: Inclusion Criteria

Patients are eligible 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.

Exclusion Criteria Patients are ineligible if they have:

1. received a 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; or
6. known severe coronary artery calcification (calcium score \>1000).

Primary outcomes:
measure: Rate of normal/non-obstructive CAD diagnosed through ICA
timeFrame: 90 days (after randomization)
Secondary outcomes:
measure: Quantitative assessment of number of angiograms avoided
timeFrame: 90 days (after randomization)
measure: Deviation from management recommendations following CCTA (i.e. angiograms performed when not recommended)
timeFrame: 90 days (after randomization)
measure: Diagnostic yield of invasive angiography
timeFrame: 90 days (after randomization)
measure: Sex differences in rate of normal/non-obstructive CAD diagnosed through ICA
timeFrame: 90 days (after randomization)
measure: Site differences in rate of normal/non-obstructive CAD diagnosed through ICA
timeFrame: 90 days (after randomization)
measure: Budget impact of new strategy for risk stratification of CAD in low-risk patients
timeFrame: 90 days (after randomization)
measure: Number of low-quality CCTAs
timeFrame: 90 days (after randomization)
Other outcomes:
Locations:
city: Hamilton
country: Canada
facility: Hamilton General Hospital
state: Ontario
city: Hamilton
country: Canada
facility: McMaster University Medical Centre
state: Ontario
city: St. Catharines
country: Canada
facility: St. Catharines Hospital
state: Ontario
Study officials:
affiliation: Hamilton Health Sciences Corporation
name: Jon-David Schwalm, MD, MSc
role: PRINCIPAL_INVESTIGATOR
affiliation: Hamilton Health Sciences Corporation
name: Jeremy Petch, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Niagara Health
name: Natalia Pinilla-Echeverri, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.