Predicting the spontaneous cardioversion of atrial fibrillation using artificial intelligence–enabled electrocardiography
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2025
Authors
Wadforth, B.
Shahrbabaki, S.S.
Strong, C.
Karnon, J.
Goh, J.S.
O’Loughlin, L.P.
Tonchev, I.
Mitchell, L.
Strube, T.
Lorensini, S.
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European Heart Journal: Digital Health, 2025; 6(5):969-978
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Brandon Wadforth, Sobhan Salari Shahrbabaki, Campbell Strong, Jonathan Karnon, Jing Soong Goh, Luke Phillip O'Loughlin, Ivaylo Tonchev, Lewis Mitchell, Taylor Strube, Scott Lorensini, Darius Chapman, Evan Jenkins, Anand N. Ganesan
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Aims Spontaneous cardioversion (SCV) is commonly observed in patients presenting to emergency departments (EDs) with primary atrial fibrillation (AF). Predicting SCV could facilitate timely discharge and avoid costly admissions. We sought to evaluate whether SCV could be predicted using artificial intelligence–enabled electrocardiograms (AI-ECGs) and whether this could produce cost savings. Methods and results We recruited patients presenting to EDs with primary AF throughout 2022–23. Patients were excluded if the outcome of their AF episode was unclear, or the ECG was not accessible. Spontaneous cardioversion prediction was attempted using ResNet50, EfficientNet, and DenseNet convolutional neural network (CNN) architectures and subsequently an ensemble learning model. We then performed a cost-minimization analysis to estimate the cost effect of a prediction-guided ‘wait-and-see’ protocol. There were 1159 presentations to the ED, of which 502 had sufficient data for inclusion. The median age was 74.0 years and 54.0% were women. Spontaneous cardioversion occurred in 227 (45.2%) patients and was more frequent in younger patients (P < 0.001). The ensemble learning model outperformed individual CNNs, achieving an accuracy of 69.7% (SD 5.91) and a receiver operating characteristic area under the curve (ROC AUC) of 0.742 (SD 0.037) with a sensitivity and specificity of 0.736 (SD 0.068) and 0.657 (SD 0.150), respectively. The per patient cost was $4681 if all patients were admitted, which reduced to $3398 with a prediction-guided ‘wait-and-see’ protocol with a 33.3% reduction in overall hospitalization. Conclusion Artificial intelligence–enabled electrocardiogram can predict SCV in patients presenting to EDs with primary AF, and a prediction-guided ‘wait-and-see’ protocol utilizing AI-ECG could lead to substantial cost savings and reduced hospitalization.
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© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.