Identifying heart failure dynamics using multi-point electrocardiograms and deep learning.

Abstract

Aims: Heart failure (HF) hospitalizations are associated with poor survival outcomes, emphasizing the need for early intervention. Deep learning algorithms have shown promise in HF detection through electrocardiogram (ECG). However, their utility in ongoing HF monitoring remains uncertain. This study developed a deep learning model using 12-lead ECGs collected at 2 different time points to evaluate HF status changes, aiming to enhance early intervention and continuous monitoring in various healthcare settings.

Methods and results: We analysed 30 171 ECGs from 6531 adult patients at Kobe University Hospital. The participants were randomly assigned to training, validation, and test datasets. A Transformer-based model was developed to classify HF status into deteriorated, improved, and no-change classes based on ECG waveform signals at two different time points. Performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and accuracy, were calculated, and attention mapping via gradient-weighted class activation mapping was utilized to interpret the model's decision-making ability. The patients had an average age of 64.6 years (±15.4 years) and brain natriuretic peptide of 66.3 pg/mL (24.6-175.1 pg/mL). For HF status classification, the model achieved an AUROC of 0.889 [95% confidence interval (CI): 0.879-0.898] and an accuracy of 0.871 (95% CI: 0.864-0.878).

Conclusion: Transformer-based deep learning model demonstrated high accuracy in detecting HF status changes, highlighting its potential as a non-invasive, efficient tool for HF monitoring. The reliance of the model on ECGs reduces the need for invasive, costly diagnostics, aligning with clinical needs for accessible HF management.

Irb information: Kobe University Hospital Clinical & Translational Research Center (reference number: B220208).