Coronary Artery Disease (CAD)
Conditions
Keywords
Artificial Intelligence, Electrocardiogram, Coronary Artery Disease
Brief summary
The study objective is to evaluate the effectiveness of the ECGio algorithm in predicting clinically significant coronary artery disease . ECGio's diagnostic performance during the trial will be compared against an objective performance ¬criteria using a mixed reference standard of quantitative coronary angiography and quantitative coronary computed tomography angiography in patients a general adult population under suspicion of coronary artery disease.
Interventions
DEVICEAI-ECG Analysis
The AI-Analysis done on the ECGs in a retrospective fashion
Sponsors
Heart Input Output Inc
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 89 Years
Healthy volunteers
No
Inclusion criteria
1. Patients 18 years of age or older at time of data collection. 2. Patients with medical records stored in a digitized format. 3. Patients under suspicion of coronary artery disease (both suspicion of significant coronary artery disease as well as to rule out significant CAD) who present to the site with an electrocardiogram recorded up to 30 days prior to Coronary Computed Tomography Angiography.
Exclusion criteria
1. Patients with acute coronary syndrome. 2. Patients who previously underwent coronary artery bypass grafting. 3. Patients whose electrocardiogram tracing has extreme noise or artifact to the extent that it would be recommended to redo the tracing. 4. Patients with prior percutaneous coronary intervention resulting in stenting. 5. Unanalyzable invasive coronary angiogram. 6. Unanalyzable Coronary Computed Tomography Angiography. 7. Unanalyzable electrocardiogram signal. 8. Incomplete invasive coronary angiogram (e.g., only the right coronary artery was injected and visualized). 9. Patient core lab analyzed Coronary Computed Tomography Angiography showed ≥ 50% blockage in any vessel but patient was not referred to invasive coronary angiogram.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity & Specificity | Within 30 days of enrollment | The lower 95% bound of ECGio's sensitivity and specificity in patients who underwent invasive angiography or computed tomography angiography (Co-primary endpoints) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity & Specificity | For the first 300 patients referred to invasive angiography through study completion, an average of 90 days | The lower 95% bound of ECGio's sensitivity and specificity in patients who underwent invasive angiography (Co-secondary endpoints) in enrollment period 2 |
| Demographic Performance | For patients in the 30 days following computed tomography angiography | ECGio's predictive performance across different demographic groups (e.g Race, Sex, Risk Factors) |
| Angiographic Stenosis Prediction | For the first 300 patients referred to invasive angiography through study completion, an average of 90 days | The Root Mean Squared Error in predicting the greatest diameter stenosis per vessel (Left Main Artery, Left Anterior Descending Artery, Left Circumflex Artery, Right Coronary Artery) |
Countries
United States
Contacts
CONTACTMichael Leasure
PRINCIPAL_INVESTIGATORGary S Mintz
CardioVascular Research Foundation
Outcome results
None listed