Predicting the development of atrial fibrillation in patients with sinus rhythm by some parameters of standard transthoracic echocardiography using a trained neural network
Authors:
Company:
1 Ryazan State Medical University named after academician I.P. Pavlov, Ryazan, Russian Federation
2 Lipetsk City Hospital No. 4 “Lipetsk-Med”, Lipetsk, Russian FederationГУЗ «Липецкая городская больница № 4 “Липецк-Мед”», Липецк, Российская Федерация
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Type: Original articles
DOI:
For citation: Kotlyarov S.N., Lyubavin A.V. Predicting the development of atrial fibrillation in patients with sinus rhythm by some parameters of standard transthoracic echocardiography using a trained neural network. Creative Cardiology. 2023; 17 (4): 481–90 (in Russ.). DOI: 10.24022/1997-3187-2023-17-4-481-490
Received / Accepted: 18.09.2023 / 24.10.2023
Keywords: atrial fibrillation neural networks transthoracic echocardiography
Abstract
Objective. Purpose of the study was to determine the possibilities of predicting the development of atrial fibrillation in patients with sinus rhythm by some parameters of standard transthoracic echocardiography using a trained neural network.
Material and methods. The study includes data from transthoracic echocardiography protocols of 695 patients treated in the cardiology department Lipetsk city Hospital No. 4 in 2022–2023. On the basis of the size of the heart chambers, the thickness of the walls of the left ventricle, the fraction of the left ventricle ejection and the severity of valvular regurgitation, the neural network was trained. Then, using a trained neural network echocardiography data of 30 patients, who underwent transthoracic echocardiography on sinus rhythm for one year before the detection of atrial fibrillation, were processed.
Results. The trained neural network successfully differentiated between patients with atrial fibrillation and those without a history of atrial fibrillation in 81.0% of cases (area under the curve (AUC) AUC 0.799, 95% confidence interval (CI) 0.738–0.932, р<0.0001) and correctly predicted the development of atrial fibrillation in patients with sinus rhythm during 3.0±0.45 months before manifestation in 80% of cases with method sensitivity of 88.9% and specificity 81.1% (AUC 0.854, 95% CI 0.738–0.932, р<0.0001).
Conclusion. Transthoracic echocardiography data identify patients with high probability of atrial fibrillation. The use of a trained neural network to predict the development of atrial fibrillation may be useful in determining indication for long-term electrocardiography-monitoring in patients with sinus rhythm in order to detect latent atrial fibrillation early.
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