Coronary disease & ACSCohort study
Use of Electrocardiograms to Identify Coronary Artery Disease
- retrospective study
- 10-fold cross-validation
- external validation set
Authors' conclusion
This study demonstrated the clinical utility of a deep learning artificial intelligence algorithm to analyze a digital 12-lead ECG to predict the presence of clinically significant CAD as determined by coronary angiography.
Study context
- Population
- Patients presenting for coronary angiography with a resting 12-lead digital electrocardiogram (ECG) recorded within 90 days priorpatients with a resting 12-lead digital ECG within 90 days prior to coronary angiography, 2019 to 2021
- Participants
- 16,476 patients
- Exposure
- Cross-validation cohort vs External validation
Further findings
Prediction of clinically significant CAD
Prevalence of clinically significant CAD
Positive predictive value Cross-validation cohort 91.7% (95% CI: 89.9%-93.4%) · External validation 82.5% (95% CI: 75.9%-89.2%)
Negative predictive value Cross-validation cohort 72.8% (95% CI: 69.6%-76.0%) · External validation 88.1% (95% CI: 84.0%-92.1%)
Area under the curve Cross-validation cohort 91.4% (95% CI: 89.4%-94.4%) · External validation 92.4% (95% CI: 89.7%-95.1%)
Original publication
Summarised from the published abstract; every number is checked against it