An artificial intelligence-based model accurately detected left ventricular systolic dysfunction from 12-lead ECGs with an AUROC of 0.88, 82% sensitivity, and 77% specificity.
Observational (n=42,291)
No
Does an AI-based model applied to 12-lead ECGs accurately detect left ventricular systolic dysfunction compared to echocardiography?
An AI-based model applied to standard 12-lead ECGs can accurately detect left ventricular systolic dysfunction, and high-probability false positives may predict future development of the condition.
Effect estimate: AUROC 0.88
Aims: The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods and results: Patients with digitalized data pairs of 12-lead ECGs and echocardiography (at intervals of ≤7 days) were retrospectively selected from the Heart Center Leipzig ECG and electronic medical records databases. A previously developed AI-based model was applied to ECGs and calculated probabilities for LVSD. The area under the receiver operating characteristic curve (AUROC) was computed overall and in cohorts stratified for baseline and ECG characteristics. Repeated echocardiography studies recorded ≥3 months after index diagnostics were used for follow-up (FU) analysis. At baseline, 42 291 ECG-echocardiography pairs were analysed, and AUROC for LVSD detection was 0.88. Sensitivity and specificity were 82% and 77% for the optimal LVSD probability cut-off based on Youden's J. AUROCs were lower in ECG subgroups with tachycardia, atrial fibrillation, and wide QRS complexes. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a four-fold increased risk of developing LVSD during FU. Conclusion: We provide the external validation of an existing AI-based ECG-analysing model for the detection of LVSD with robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU deserves a further evaluation in prospective trials.
König et al. (Tue,) conducted a observational in Left ventricular systolic dysfunction (n=42,291). Artificial intelligence-based model for 12-lead ECGs vs. Echocardiography was evaluated on Detection of left ventricular systolic dysfunction (AUROC 0.88). An artificial intelligence-based model accurately detected left ventricular systolic dysfunction from 12-lead ECGs with an AUROC of 0.88, 82% sensitivity, and 77% specificity.