Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CT Angiogram | This is the Cohort which received only Computed Tomography Angiography as a part of the study |
| |
| Invasive Angiography | This is the Cohort which received both Computed Tomography Angiography and Invasive Angiography as a part of the study |
| |
| Enrollment Period 2 | This Cohort is the continuation of enrollment of invasive angiogram patients beyond the completion of the primary endpoint |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-ECG Analysis | Device | The AI-Analysis done on the ECGs in a retrospective fashion |
|
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity & Specificity | The lower 95% bound of ECGio's sensitivity and specificity in patients who underwent invasive angiography or computed tomography angiography (Co-primary endpoints) | Within 30 days of enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity & Specificity | The lower 95% bound of ECGio's sensitivity and specificity in patients who underwent invasive angiography (Co-secondary endpoints) in enrollment period 2 | For the first 300 patients referred to invasive angiography through study completion, an average of 90 days |
| Demographic Performance |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
The study population will be made up of all-comers to Coronary Computed Tomography Angiography from each site taking up to the first 250, consecutive patients recruited for the analysis.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Michael Leasure | Contact | 6104517343 | Michael.Leasure@heartio.ai |
| Name | Affiliation | Role |
|---|---|---|
| Gary S Mintz | CardioVascular Research Foundation | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Medstar Washington Hospital Center | Washington D.C. | District of Columbia | 20010 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34419615 | Background | Leasure M, Jain U, Butchy A, Otten J, Covalesky VA, McCormick D, Mintz GS. Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram. Can J Cardiol. 2021 Nov;37(11):1715-1724. doi: 10.1016/j.cjca.2021.08.005. Epub 2021 Aug 20. |
Not provided
Not provided
This data cannot be shared due to individual site data retention policies
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
ECGio's predictive performance across different demographic groups (e.g Race, Sex, Risk Factors) |
| For patients in the 30 days following computed tomography angiography |
| Angiographic Stenosis Prediction | 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) | For the first 300 patients referred to invasive angiography through study completion, an average of 90 days |
| Cena Research Institute | Houston | Texas | 77055 | United States |
|
| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |