Automatically optimized vectorcardiographic features are associated with recurrence of atrial fibrillation after electrical cardioversion

. 2025 Jan 08 ; 15 (1) : 1257. [epub] 20250108

Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid39779792

Grantová podpora
RVO:68081731 Akademie Věd České Republiky
RVO:68081731 Akademie Věd České Republiky
Cooperatio - Cardiovascular Science Univerzita Karlova v Praze
ANR-10-IAHU-04 Agence Nationale de la Recherche
LX22NPO5104 Ministerstvo Školství, Mládeže a Tělovýchovy

Odkazy

PubMed 39779792
PubMed Central PMC11711394
DOI 10.1038/s41598-025-85340-4
PII: 10.1038/s41598-025-85340-4
Knihovny.cz E-zdroje

Electrical cardioversion presents one of the treatment options for atrial fibrillation (AF). However, the early recurrence rate is high, reaching ~40% three months after the procedure. Features based on vectorcardiographic signals were explored to find association with early recurrence of AF. Eighty-four patients with non-paroxysmal AF referred to electrical cardioversion were prospectively studied; early AF recurrence was present in 40 (47.6%). Patients underwent 24-h Holter ECG monitoring three months after the procedure to assess AF recurrence. Pre-procedural 12-lead ECGs (10 s, 1 kHz) were recorded and automatically analyzed. We explored associations of VCG-based features with early AF recurrence. Two features were strongly associated with AF recurrence: (1) a mean VCG (y-axis) signal slope in a window starting 145 ms before QRS center, lasting for 190 ms (AUC 0.778, p < 0.001), and (2) a mean VCG (z-axis) signal slope in a window starting 60 ms after QRS center, lasting for 465 ms (AUC 0.744, p < 0.001). These features showed higher association to the outcome than eighteen baseline clinical features. Our approach revealed features based on a slope of vectorcardiographic signals. This work also suggests that state of ventricles strongly affects the AF recurrence after electrical cardioversion.

Zobrazit více v PubMed

Chugh, S. S. et al. Worldwide epidemiology of atrial fibrillation: A Global Burden of Disease 2010 Study. Circulation129(8), 837–847 (2014). PubMed PMC

Vikman, S. et al. Heart rate variability and recurrence of atrial fibrillation after electrical cardioversion Heart rate variability and recurrence of atrial ®brillation after electrical cardioversion. Ann. Med.35, 36–42 (2009). PubMed

Purkayastha, P., Ibrahim, A., Haslen, D. & Gamma, R. The efficacy and safety of a nurse-led electrical cardioversion service for atrial fibrillation over a 2-year time period. Eur. J. Cardiovasc. Nurs.22(4), 425–429 (2023). PubMed

Noseworthy, P. A. et al. Risk of stroke after catheter ablation versus cardioversion for atrial fibrillation: A propensity-matched study of 24,244 patients. Hear. Rhythm12(6), 1154–1161 (2015). PubMed

Döring, C. et al. The impact of right atrial size to predict success of direct current cardioversion in patients with persistent atrial fibrillation. Korean Circ. J.53(5), 331–343 (2023). PubMed PMC

Alcaraz, R., Hornero, F. & Rieta, J. J. Noninvasive time and frequency predictors of long-standing atrial fibrillation early recurrence after electrical cardioversion. Pacing Clin. Electrophysiol.34(10), 1241–1250 (2011). PubMed

Ivora, A. et al. QRS detection and classification in Holter ECG data in one inference step. Sci. Rep. I12, 12641 (2022). PubMed PMC

Kors, J. A., Van Herpen, G., Sittig, A. C. & Van Bemmel, J. H. Reconstruction of the frank vectorcardiogram from standard electrocardiographic leads: Diagnostic comparison of different methods. Eur. Heart J.11(12), 1083–1092 (1990). PubMed

Plesinger, F. et al. VDI Vision - Analysis of ventricular electrical dyssynchrony in real-time. Comput. Cardiol.10.23919/CinC53138.2021.9662916 (2021).

Plesinger, F. et al. Fully automated QRS area measurement for predicting response to cardiac resynchronization therapy. J. Electrocardiol.63, 159–163. 10.1016/j.jelectrocard.2019.07.003 (2020). PubMed

Van Rossum, G. et al. Python 3 reference manual. CreateSpace (2009).

Harris, C. R. et al. Array programming with NumPy. Nature585(7825), 357–362 (2020). PubMed PMC

McKinney, W. Data structures for statistical computing in Python. In Proceedings 9th Python Sci. Conference 56–61 (2010).

Virtanen, P. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods17(3), 261–272 (2020). PubMed PMC

Waskom, M. L. seaborn: Statistical data visualization. J. Open Source Softw.6(60), 3021 (2021).

Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng.9(3), 90–95 (2007).

Canpolat, U. et al. Association of fragmented QRS with left atrial scarring in patients with persistent atrial fibrillation undergoing radiofrequency catheter ablation. Hear. Rhythm17(2), 203–210 (2020). PubMed

Eren, H., Kaya, Ü., Öcal, L., Şenbaş, A. & Kalçık, M. The presence of fragmented QRS may predict the recurrence of nonvalvular atrial fibrillation after successful electrical cardioversion. Ann. Noninvas. Electrocardiol.25(1), e12700, 10.1111/anec.12700 (2020). PubMed PMC

Keskin, H. A. & Kurtul, A. Fragmented QRS complexes are associated with postoperative atrial fibrillation development after coronary artery bypass grafting surgery. Coron. Artery Dis.32(1), 58–63 (2021). PubMed

McLellan, A. J. A. et al. Diffuse ventricular fibrosis measured by T1 mapping on cardiac MRI predicts success of catheter ablation for atrial fibrillation. Circ. Arrhythmia Electrophysiol.7(5), 834–840 (2014). PubMed

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...