Clinical Approach to Atrial Tachycardia and Atrial Flutter From an Understanding of the Mechanisms. Electrophysiology Based on Anatomy

García-Cosío, Francisco; Fuentes, Agustín; Angulo, Ambrosio Núñez

doi:10.1016/j.rec.2011.11.013

Cited by 10 publications

(12 citation statements)

References 62 publications

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“…25 Additionally, individuals with flutter were more likely to have a prolonged PR interval, which might promote the atrial anatomic or functional conduction delay that is a prerequisite for a macro-reentrant arrhythmia such as flutter. 12 Nevertheless, the overlap of risk factors of flutter and AF may partially explain why patients with flutter have a high risk of AF. 26,27 …”

Section: Discussionmentioning

confidence: 99%

“…Possible atypical flutter (n=18) cases were excluded from our analyses due to distinct underlying electrophysiological mechanisms from typical flutter, the more common type of flutter. 12 All 100 ECGs previously diagnosed as AF were confirmed to be AF. Any disagreement between the cardiologists was resolved by joint review of the ECG.…”

Section: Methodsmentioning

confidence: 99%

“…10,11 Atypical flutter describes a wide-variety of re-entry circuits in either the left or right atrium (not involving the cavotricuspid isthmus) and can be difficult to diagnose on electrocardiogram alone. 8,12 In comparison, disorganized, uncoordinated atrial activity is seen in AF. We hypothesized that although there would be overlap between risk factors for typical flutter and AF, there would also be differences due to the varying underlying electrophysiological mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

et al. 2016

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Background Few epidemiological cohort studies have evaluated atrial flutter (flutter) as an arrhythmia distinct from atrial fibrillation (AF). Objective To examine the clinical correlates of flutter and its associated outcomes to distinguish them from those associated with AF in the Framingham Heart Study. Methods We reviewed and adjudicated electrocardiograms previously classified as flutter or AF/flutter and another 100 electrocardiograms randomly selected from AF cases. We examined the clinical correlates of flutter by matching up to 5 AF and 5 referents to each flutter case using a nested case-referent design. We determined the 10-year outcomes associated with flutter with Cox models. Results During mean follow-up of 33.0±12.2 years, 112 participants (mean age 72±10 years, 30% women) developed flutter. In multivariable analyses, smoking (odds ratio [OR] 2.84; 95% confidence interval [CI], 1.54 to 5.23), increased PR interval (OR 1.28 per SD; 95% CI, 1.03 to 1.60), myocardial infarction (OR 2.25; 95% CI, 1.05 to 4.80) and heart failure (OR 5.22; 95% CI, 1.26 to 21.64) were associated with incident flutter. In age- and sex-adjusted models, flutter (vs. referents) was associated with 10-year increased risk of AF (hazard ratio [HR] 5.01; 95% CI, 3.14 to 7.99), myocardial infarction (HR 3.05; 95% CI, 1.42 to 6.59), heart failure (HR 4.14; 95% CI, 1.90 to 8.99), stroke (HR 2.17; 95% CI, 1.13 to 4.17), and mortality (HR 2.00; 95% CI, 1.44 to 2.79). Conclusions We identified the clinical correlates associated with flutter and observed that flutter was associated with multiple adverse outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

et al. 2016

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“…Reentrant propagation of electrical activity plays a decisive role in the perpetuation of atrial tachy-arrhythmias. While atrial flutter (AFL) is caused by a macroreentrant circuit around anatomical and/or functional obstacles that can be terminated ablating the critical isthmus 1 , the nature of the waves' mechanisms that maintain atrial fibrillation (AF) is still controversial 2 . Fibrillatory electrograms analyzed using rules developed for organized rhythms, such as activation mapping 2 , are unreliable due to inconsistencies in the estimated activation times in relation to the presence of farfield intra-chamber crosstalk, noise or multicomponent EGMs 3 .…”

Section: Introductionmentioning

confidence: 99%

Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms

Rodrigo

Climent

Liberos

et al. 2017

Circ: Arrhythmia and Electrophysiology

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Background: Phase mapping has become a broadly used technique to identify atrial reentrant circuits for ablative therapy guidance. This work studies the phase mapping process and how the signal nature and its filtering affect the reentrant pattern characterization in EGM, Body Surface Potential Mapping (BSPM) and Electrocardiographic Imaging (ECGI) signals. Methods and Results: EGM, BSPM and ECGI phase maps were obtained from 17 simulations of atrial fibrillation (AF), atrial flutter (AFL) and focal atrial tachycardia (AT). Reentrant activity was identified by singularity point recognition in raw signals and in signals after narrow band-pass filtering at the Highest Dominant Frequency (HDF). Reentrant activity was dominantly present in the EGM recordings only for AF and some AFL propagations patterns, and HDF filtering allowed increasing the reentrant activity detection from 60% to 70% of time in AF in unipolar recordings and from 0% to 62% in bipolar. In BSPM maps, HDF filtering increased from 10% to 90% the sensitivity, although provoked a residual false reentrant activity ~30% of time. In ECGI, HDF filtering allowed to increase up to 100% the time with detected rotors, although provoked the apparition of false rotors during 100% of time. Nevertheless, raw ECGI phase maps presented reentrant activity just in AF recordings accounting for ~80% of time. Conclusions: Rotor identification is accurate and sensitive and does not require additional signal processing in measured or noninvasively computed unipolar EGMs. Bipolar EGMs and BSPM do require HDF filtering in order to detect rotors at the expense of a decreased specificity.

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“…The influence of the atrial anatomy on the arrhythmias from the clinical electrophysiological point of view have been summarized recently in the review article by Garcia-Cosio et al. [16]. The latest computational approaches to guiding arrhythmia therapy are presented in a recent review by Roberts et al.…”

Section: Introductionmentioning

confidence: 99%

A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included

Podziemski

Żebrowski

2013

J Clin Monit Comput

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Existing atrial models with detailed anatomical structure and multi-variable cardiac transmembrane current models are too complex to allow to combine an investigation of long time dycal properties of the heart rhythm with the ability to effectively simulate cardiac electrical activity during arrhythmia. Other ways of modeling need to be investigated. Moreover, many stateof-the-art models of the right atrium do not include an atrioventricular node (AVN) and only rarely-the sinoatrial node (SAN). A model of the heart tissue within the right atrium including the SAN and AVN nodes was developed. Looking for a minimal model, currently we are testing our approach on chosen well-known arrhythmias, which were until now obtained only using much more complicated models, or were only observed in a clinical setting. Ultimately, the goal is to obtain a model able to generate sequences of RR intervals specific for the arrhythmias involving the AV junction as well as for other phenomena occurring within the atrium. The model should be fast enough to allow the study of heart rate variability and arrhythmias at a time scale of thousands of heart beats in real-time. In the model of the right atrium proposed here, different kinds of cardiac tissues are described by sets of different equations, with most of them belonging to the class of Liénard nonlinear dynamical systems. We have developed a series of models of the right atrium with differing anatomical simplifications, in the form of a 2D mapping of the atrium or of an idealized cylindrical geometry, including only those anatomical details required to reproduce a given physiological phenomenon. The simulations allowed to reconstruct the phase relations between the sinus rhythm and the location and properties of a parasystolic source together with the effect of this source on the resultant heart rhythm. We model the action potential conduction time alternans through the atrioventricular AVN junction observed in cardiac tissue in electrophysiological studies during the ventricular-triggered atrial tachycardia. A simulation of the atrio-ventricular nodal reentry tachycardia was performed together with an entrainment procedure in which the arrhythmia circuit was located by measuring the post-pacing interval (PPI) at simulated mapping catheters. The generation and interpretation of RR times series is the ultimate goal of our research. However, to reach that goal we need first to (1) somehow verify the validity of the model of the atrium with the nodes included and (2) include in the model the effect of the sympathetic and vagal ANS. The current paper serves as a partial solution of the 1). In particular we show, that measuring the PPI-TCL entrainment response in proximal (possibly-the slow-conducting pathway), the distal and at a mid-distance from CS could help in rapid distinction of AVNRT from other atrial tachycardias. Our simulations support the hypothesis that the alternans of the conduction time between the atria and the ventricles in the AV orthodromic reciprocating t...

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Clinical Approach to Atrial Tachycardia and Atrial Flutter From an Understanding of the Mechanisms. Electrophysiology Based on Anatomy

Cited by 10 publications

References 62 publications

Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms

A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included

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