Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning.

Autor: Călburean PA; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium.; University of Medicine, Pharmacy, Science and Technology 'George Emil Palade' of Târgu Mureş Târgu Mureş Romania., Pannone L; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Monaco C; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Rocca DD; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Sorgente A; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Almorad A; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Bala G; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Aglietti F; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Ramak R; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Overeinder I; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Ströker E; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Pappaert G; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Măru'teri M; University of Medicine, Pharmacy, Science and Technology 'George Emil Palade' of Târgu Mureş Târgu Mureş Romania., Harpa M; University of Medicine, Pharmacy, Science and Technology 'George Emil Palade' of Târgu Mureş Târgu Mureş Romania., La Meir M; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Brugada P; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Sieira J; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Sarkozy A; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., Chierchia GB; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium., de Asmundis C; Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium.
Jazyk: angličtina
Zdroj: Journal of the American Heart Association [J Am Heart Assoc] 2024 May 21; Vol. 13 (10), pp. e033148. Date of Electronic Publication: 2024 May 10.
DOI: 10.1161/JAHA.123.033148
Abstrakt: Background: Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis.
Methods and Results: Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS-Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS-Net recognized a BrS type I pattern with an AUC-ROC of 0.945 (0.921-0.969) and an AUC-PR of 0.892 (0.815-0.939). When trained and evaluated on ECG tracings at baseline, BrS-Net predicted a BrS type I pattern during ajmaline with an AUC-ROC of 0.805 (0.845-0.736) and an AUC-PR of 0.605 (0.460-0.664).
Conclusions: BrS-Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS-Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.
Databáze: MEDLINE
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