Signal-averaged P-wave ECG discriminates between persistent and paroxysmal atrial fibrillation.
Atrial fibrillation (Afib) has been associated with abnormal signal-averaged P wave (SAPW) parameters but whether SAPW is associated with the time course of these arrhythmias (persistent vs. paroxysmal, frequent vs. rare episodes) remains unknown. This article assesses the significance of SAPW duration in distinguishing patients with paroxysmal from those with persistent Afib. SAPW electrocardiogram was performed with a "double triggering" software system for acquisition and analysis of data. SAPW total duration (TD), X, Y, and Z leads magnitudes and vector signal magnitudes were measured offline. Optimal cutoff value between control and patient groups was determined by using a receiver operator characteristic curve. Data were analyzed to compare controls with patients groups and between patient groups according to time course of the arrhythmic history. Fifteen age-matched patients without a history of Afib and 55 patients with a history of Afib (33 with paroxysmal and 22 with persistent arrhythmia) were studied off antiarrhythmic drugs. Patients with history of persistent arrhythmia were studied within 2 days after electrical cardioversion to sinus rhythm. SAPW TD was the only parameter consistently associated with Afib. SAPW TD was 98 +/- 6 ms (mean +/- SD) in the control group, 127 +/- 19 ms in the pooled patients group (P =.0001); 122 +/- 20 ms for paroxysmal and 133 +/- 17 ms for the persistent Afib group (P =.04). Analysis of other SAPW parameters were not statistically significant. SAPW duration is a simple method to identify patients with history of Afib. A significantly longer SAPW TD characterizes patients with a history of persistent as opposed to paroxysmal Afib. Prolongation of SAPW TD possibly reflects more advanced electrophysiological changes, creating a more stable substrate that leads to persistence of the arrhythmia.
Santoni-Rugiu
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Electrophysiology Section of the Zena and Micheal Wiener Cardiovascular Institute, Department of Medicine, The Mount Sinai Medical Center and The Mount Sinai School of Medicine of NYU, New York, NY 10029, USA. anthony_gomes@mssm.edu
Verma
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Mehta
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Gopal
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Chan
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Gomes
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D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D016022:Case-Control Studies; D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted