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Updated: May 15, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Clinical and Electrophysiological Predictors of Isthmus Dependency in Atrial Flutter
Lyuboslav Katov1, Sonja Reiländer1, Alyssa Schlarb1
1Department of Cardiology, Ulm University Heart Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
Abstract:
Background: Atrial flutter (AFL) is a macro-reentrant tachycardia classified as cavotricuspid isthmus (CTI)-dependent or non-CTI-dependent based on its reliance on the CTI for conduction. CTI dependence can present as type I ECG (sawtooth flutter waves in inferior leads and positive P-waves in V1) or type II ECG (absence of these characteristics). This study aimed to identify clinical and electrophysiological parameters to improve CTI dependence prediction in AFL. Methods: Patients at the Ulm University Heart Center between 2010 and 2019 with AFL undergoing electrophysiological studies and ablation were enrolled. Clinical and electrophysiological parameters such as age, gender, prior comorbidities, interventions, and medication use were analyzed. Results: The study included 383 patients, with 70% presenting with type I ECG AFL. CTI dependence was observed in 242 (90.3%) type I ECG patients and 52 (45.2%) type II ECG patients. CTI-dependent AFL patients were younger and had fewer comorbidities. Predictors for CTI dependence in type I ECG included male gender (p = 0.006), absence of beta-blocker use (p = 0.031), no prior atrial fibrillation (p = 0.035), and no prior pulmonary vein isolation (p < 0.001). In type II ECG, predictors for CTI dependence included younger age (p = 0.016), male gender (p = 0.007), absence of arterial hypertension (p = 0.036), and longer atrial cycle length (p < 0.001). Conclusions: Identifying clinical and electrophysiological parameters enhances the ability to predict CTI dependence in AFL, offering valuable insights for tailored diagnostic and therapeutic approaches. Coupling these parameters with ECG findings holds promise for refining prediction accuracy and optimizing patient care.
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