Abstract
Abstract Background Heart failure with mildly reduced ejection fraction (HFmrEF) has emerged as the predominant subtype of heart failure (HF), but there is a paucity of data for identifying it using artificial intelligence (AI) based electrocardiogram (ECG). Purpose This study aimed to develop artificial intelligence (AI)-ECG to identify and predict the prognosis of patients with HFmrEF. Methods We collected 104,336 12-lead electrocardiography (ECG) datasets from April 2009 to December 2021 in a tertiary center. The AI-ECG encompasses a novel model that combines an automatic labelling preprocessing method with a transformer architecture incorporating a triplet loss for HFmrEF analysis. Results The receiver operating characteristic analyses revealed that the area under the curve of AI-ECG for identifying all types of HF were acceptable (0.87, 95% confidence interval [CI]: 0.86-0.89), but that for identifying those with HFmrEF was relatively lower (0.83, 95% CI: 0.78-0.86) than those with HF with reduced ejection fraction (HFrEF) (0.89, 95% CI: 0.85-0.82) and those with normal ejection fraction (EF) (0.87, 95% CI: 0.85-0.89). The analysis of ECG features showed significant increases in QRS duration (p=0.001), QT interval (p=0.045), and corrected QT interval (p=0.041) with increasing ‘Severity by Euclidean distance’. Following the predictability analysis with another group of 1,134 patients for improvements of follow-up EF in HFmrEF, the patients were grouped into three clusters based on the AI- Euclidean distance; Cluster 1 had the most severe cases and poorer outcomes than Clusters 2 (p<0.001) and 3 (p<0.001). Conclusion AI-ECG presents an innovative approach for the prognostic stratification of cardiac contractility in patients with HFmrEF. By using AI-ECG, patients with HFmrEF will be able to predict upcoming disease progression.
Published Version
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