Coronary Artery Disease
Conditions
Keywords
Artificial Intelligence (AI), Coronary Angiography, Coronary Computed Tomography Angiography
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.
Interventions
OTHERUsual 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.
OTHERCentralized 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.
Sponsors
Hamilton Health Sciences Corporation
Population Health Research Institute
Hamilton Academic Health Sciences Organization
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Rate of normal/non-obstructive CAD diagnosed through ICA | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Quantitative assessment of number of angiograms avoided | 90 days (after randomization) | Number of angiograms avoided due to CCTA bookings. |
| Deviation from management recommendations following CCTA (i.e. angiograms performed when not recommended) | 90 days (after randomization) | Number of angiograms performed when not recommended. |
| Diagnostic yield of invasive angiography | 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). |
| Sex differences in rate of normal/non-obstructive CAD diagnosed through ICA | 90 days (after randomization) | Difference in the rate of normal/non-obstructive CAD diagnosed through ICA between males and females. |
| Site differences in rate of normal/non-obstructive CAD diagnosed through ICA | 90 days (after randomization) | Difference in the rate of normal/non-obstructive CAD diagnosed through ICA between sites. |
| Budget impact of new strategy for risk stratification of CAD in low-risk patients | 90 days (after randomization) | Cost of risk stratification of CAD in low risk patients. |
| Number of low-quality CCTAs | 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. |
Countries
Canada
Contacts
PRINCIPAL_INVESTIGATORJon-David Schwalm, MD, MSc
Hamilton Health Sciences Corporation
PRINCIPAL_INVESTIGATORJeremy Petch, PhD
Hamilton Health Sciences Corporation
PRINCIPAL_INVESTIGATORNatalia Pinilla-Echeverri, MD, PhD
Niagara Health
Outcome results
None listed