Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction

Perez-Downes, Julio; Fitzgerald, Patrick; Adedinsewo, Demilade; Carter, Rickey E.; Noseworthy, Peter A.; Kusumoto, Fred

doi:10.1161/circep.121.009871

Cited by 2 publications

(1 citation statement)

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“…This is in line with findings from other working groups and models. 26 , 34 , 35 Since the clinical diagnoses of atrial fibrillation or existing (left) bundle branch block are likely to be considered as potential indicators of structural heart disease by clinicians, an echocardiography will be performed in most cases either way. Therefore, the inferior discriminatory power of AI models related to these ECG patterns should not be relevant in the context of population screening for LVSD.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model

König,

Hohenstein,

Nitsche

et al. 2023

European Heart Journal - Digital Health

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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.

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Section: Discussionmentioning

confidence: 99%