Key result
AI-based 12-lead ECG model detects LVSD with an AUROC of ~0.88.
Why the study?
Most published AI-based algorithms for detecting cardiovascular diseases from ECGs lack external validation, limiting their clinical applicability.
Does an AI-based model applied to 12-lead ECGs accurately detect left ventricular systolic dysfunction compared to echocardiography?
Observational (n=42,291)
No
Does an AI-based model applied to 12-lead ECGs accurately detect left ventricular systolic dysfunction compared to echocardiography?
Effect estimate: AUROC 0.88
An existing open-source AI algorithm demonstrates high accuracy in detecting left ventricular systolic dysfunction from standard 12-lead ECGs, and false-positive results may identify patients at high risk for future deterioration of ventricular function.
May support ECG-based LVSD screening in select settings; leaves open prospective outcome validation before practice change.
Background The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECG) evolves and some promising results were reported. However, most published algorithms lack external validation. Purpose Aim of this study was to validate an existing, open-source algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods 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. 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. Results At baseline, 42,291 ECG-echocardiography pairs were analyzed and AUROC for LVSD detection was 0.88. Corresponding sensitivity and specificity were 82% and 77% for the optimal LVSD-probability cutoff based on Youden’s J. AUROCs were lower in ECG-subgroups with tachycardia, atrial fibrillation and wide QRS complex. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a 4-fold increased risk of developing LVSD during FU. Conclusion We provide the external validation of an existing AI-based ECG-analyzing model for the detection of LVSD with excellent and robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU is hypothesis-generating and deserves further research.
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Koenig et al. (2023) conducted an observational in Left ventricular systolic dysfunction (n=42,291). Artificial intelligence-based ECG-analyzing model vs. Echocardiography was evaluated on Detection of left ventricular systolic dysfunction (AUROC 0.88). An AI-based model for 12-lead ECGs detected left ventricular systolic dysfunction with an AUROC of 0.88, demonstrating 82% sensitivity and 77% specificity.
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