Key result
AI-enabled ECG models outperformed echocardiography in predicting new-onset cardiovascular diseases associated with severe left atrial enlargement (LAD ≥50 mm).
Why the study?
The role of the P wave in identifying left atrial enlargement using AI-enabled ECG models was unclear, and it was unknown whether single-lead ECG models could serve as a diagnostic tool for surveillance.
Do artificial intelligence-enabled ECG models accurately identify left atrial enlargement and predict long-term cardiovascular risk compared to echocardiography in adults?
Observational (n=382,594)
Yes
Do artificial intelligence-enabled ECG models accurately identify left atrial enlargement and predict long-term cardiovascular risk compared to echocardiography in adults?
AI-enabled ECG models utilizing P-wave segmentation can accurately identify left atrial enlargement and may outperform echocardiography in predicting associated future cardiovascular diseases.
May support AI-ECG screening for severe LA enlargement risk; leaves open prospective validation against echocardiography.
Background The role of P wave in identifying left atrial enlargement (LAE) using artificial intelligence (AI)-enabled electrocardiogram (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECGs could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and/or non-SR ECGs, and compared the prognostic ability of severe LAE as left atrial diameter (LAD) ≥50 mm assessed by AI-enabled ECG models and echocardiography. Methods This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiograms measured within 2 weeks at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed using data from 11,753 patients from another hospital. Results The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at an LAD cut-off 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR dataset, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than AI-enabled ECG-severe LAE. Conclusions P wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.
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Chou et al. (2023) conducted an observational in Left atrial enlargement (n=382,594). AI-enabled ECG models vs. Echocardiography was evaluated on Identification of left atrial enlargement and prediction of future cardiovascular disease. AI-enabled ECG models outperformed echocardiography in predicting new-onset cardiovascular diseases associated with severe left atrial enlargement (LAD ≥50 mm).
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