Key result
Deep learning on noisy single-lead ECGs detects moderate to severe AS with ~90% sensitivity.
Why the study?
Aortic stenosis is often diagnosed late due to lack of feasible screening, but portable and wearable single-lead ECGs could enable scalable screening even with noisy acquisition.
Does a deep learning model applied to noisy single-lead ECGs accurately detect moderate or severe aortic stenosis in a general hospital population?
Observational (n=35,992)
No
Does a deep learning model applied to noisy single-lead ECGs accurately detect moderate or severe aortic stenosis in a general hospital population?
Effect estimate: AUROC 0.829 (95% CI 0.800-0.855)
A deep learning model can detect moderate to severe aortic stenosis from noisy single-lead ECGs with high sensitivity, offering a potentially scalable strategy for community-based screening.
May support single-lead ECG screening for aortic stenosis; leaves open prospective validation before clinical use.
Background Due to the lack of a feasible screening strategy, aortic stenosis (AS) is often diagnosed after the development of clinical symptoms, representing advanced stages of disease. Portable and wearable devices capable of recording electrocardiograms (ECGs) can be used for scalable screening for AS, if the diagnosis can be made with a single-lead ECG, despite potentially noisy acquisition. Methods Using electronic health records and imaging data from a large, diverse hospital system (2015-2022), we developed a deep learning-based approach to detect moderate/severe AS using a single-lead ECG. We used ECGs paired with echocardiograms obtained within 30 days of each other to develop the model. We extracted lead I signal data from clinical ECG and augmented it with random Gaussian noise. We trained a convolutional neural network (CNN) to identify TTE-confirmed AS using noisy single-lead ECGs. Finally, we used the CNN model probabilities, along with patient age and sex, as predictive inputs to train an extreme gradient boosting (XGBoost) model to detect moderate/severe AS. Results The model was developed in 75,901 ECGs/35,992 patients (median age 61 [interquartile range (IQR) 47-72] years, 54.3% women, 9.5% Black) and validated in 3,733 patients (median age 61 [IQR 47-72] years, 53.4% women, 9.7% Black). In the held-out validation set, the ensemble XGBoost model achieved an AUROC of 0.829 (95% CI: 0.800-0.855), with a sensitivity of 90.4% and specificity of 58.7% for detecting moderate/severe AS. For detecting severe AS, the model’s AUROC was 0.846 (95% CI, 0.778-0.899), with a sensitivity of 94.3% and specificity of 57.0%. In the test set with a 4.5% prevalence of moderate/severe AS, the model had a PPV of 9.3% and an NPV of 99.2%. In simulated cohorts with 1% and 20% prevalence of moderate/severe AS, the model’s NPVs varied from 99.8% to 96.1%, and PPV from 2.2% to 35.4%, respectively. Conclusion We developed a novel portable– and wearable-adapted deep learning approach for the detection of moderate/severe AS from noisy single-lead ECGs. Our approach represents a highly sensitive, feasible, and scalable strategy for community-based AS screening.
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Aminorroaya et al. (2023) conducted an observational in Aortic Stenosis (n=35,992). Deep learning model (XGBoost) using noisy single-lead ECG, age, and sex vs. Transthoracic echocardiography (reference standard) was evaluated on Detection of moderate/severe aortic stenosis (AUROC) (AUROC 0.829, 95% CI 0.800-0.855). A deep learning model using noisy single-lead ECGs, age, and sex detected moderate to severe aortic stenosis with an AUROC of 0.829, achieving 90.4% sensitivity and 58.7% specificity.
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