Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction.

Abstract

Background: Heart failure with mildly reduced ejection fraction (HFmrEF) has emerged as the predominant subtype of heart failure (HF). This study aimed to develop artificial intelligence (AI)-electrocardiography (ECG) to identify and predict the prognosis of patients with HFmrEF.

Methods: We collected 104,336 12-lead ECG datasets from April 2009 to December 2021 in a tertiary centre. 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 was acceptable [0.873, 95% confidence interval (CI): 0.864-0.893], while that for identifying patients with HFmrEF was relatively lower (0.824, 95% CI: 0.794-0.863) than that for those with HF with reduced ejection fraction (EF) (0.875, 95% CI: 0.844-0.912) and those with normal EF (0.870, 95% CI: 0.842-0.894). 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 953 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).

Conclusions: AI-ECG presents an innovative approach for the prognostic stratification of cardiac contractility in patients with HFmrEF. In patients with HFmrEF, disease progression can be predicted using AI-ECG.