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.
Study Type
OBSERVATIONAL
Enrollment
978
The AI-Analysis done on the ECGs in a retrospective fashion
Medstar Washington Hospital Center
Washington D.C., District of Columbia, United States
Cena Research Institute
Houston, Texas, United States
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)
Time frame: Within 30 days of enrollment
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
Time frame: For the first 300 patients referred to invasive angiography through study completion, an average of 90 days
Demographic Performance
ECGio's predictive performance across different demographic groups (e.g Race, Sex, Risk Factors)
Time frame: 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)
Time frame: For the first 300 patients referred to invasive angiography through study completion, an average of 90 days
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