Noninvasive evidence of shortened atrial refractoriness during sinus rhythm in patients with paroxysmal atrial fibrillation

CineECG provides a novel anatomical view on the normal atrial P-wave

Aims

Novel CineECG computed from standard 12-lead electrocardiogram (ECG) correlated the ventricular electric activity to ventricular anatomy. CineECG was never applied to reconstruct the spatial distribution of normal atrial electric activity into an atrial anatomic model.

Methods and results

From 6409 normal ECGs from PTB-XL database, we computed a median beat with fiducial points for P-and Q-onset. To determine the temporo-spatial location of atrial activity during PQ-interval, CineECG was computed on a normal 58-year-old male atrial/torso model. CineECG was projected to three major cardiac axes: posterior-anterior, right-left, base-roof, and to the standard cardiac four-chamber, left anterior oblique, and right anterior oblique (RAO) views. In 6409 normal subjects, during P-wave, CineECG moved homogeneously from right atrial roof towards left atrial base (-54 ± 14° in four-chamber view, 95 ± 24° RAO view). During terminal PQ-interval, the CineECG direction was opposite, moving towards left atrial roof (62 ± 27° in four-chamber view, 78 ± 27° RAO view). We identified the deflection point, where the atrial CineECG changes in direction. The time from P-onset to deflection point was similar to P-wave duration.

Conclusion

CineECG provided a novel three-dimensional visualization of atrial electrical activity during the PQ-interval, relating atrial electrical activity to the atrial anatomy. CineECG location during P-wave and terminal PQ-interval were homogeneous within normal controls. CineECG and its deflection point may enable the early detection of atrial conduction disorders predisposing to atrial arrhythmias.

Assessing Atrial Fibrillation Substrates by P Wave Analysis: A Comprehensive Review

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Pharmacologic and non-pharmacologic rhythm control strategies impact on AF-related symptoms, while leaving largely unaffected the risk of stroke. Moreover, up to 20% of AF patients are asymptomatic during paroxysmal relapses of arrhythmia, thus underlying the need for early markers to identify at-risk patients and prevent cerebrovascular accidents. Indeed, non-invasive assessment of pre-clinical substrate changes that predispose to AF could provide early identification of at-risk patients and allow for tailored care paths. ECG-derived P wave analysis is a simple-to-use and inexpensive tool that has been successfully employed to detect AF-associated structural and functional atrial changes. Beyond standard electrocardiographic techniques, high resolution signal averaged electrocardiography (SAECG), by recording microvolt amplitude atrial signals, allows more accurate analysis of the P wave and possibly AF risk stratification. This review focuses on the evidence that support P wave analysis to assess AF substrates, predict arrhythmia relapses and guide rhythm-control interventions.

Atrial Fibrillation Predictors: Importance of the Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia in adults and is associated with significant morbidity and mortality. Substantial interest has developed in the primary prevention of AF, and thus the identification of individuals at risk for developing AF. The electrocardiogram (ECG) provides a wealth of information, which is of value in predicting incident AF. The PR interval and P wave indices (including P wave duration, P wave terminal force, P wave axis, and other measures of P wave morphology) are discussed with regard to their ability to predict and characterize AF risk in the general population. The predictive value of the QT interval, ECG criteria for left ventricular hypertrophy, and findings of atrial and ventricular ectopy are also discussed. Efforts are underway to develop models that predict AF incidence in the general population; however, at present, little information from the ECG is included in these models. The ECG provides a great deal of information on AF risk and has the potential to contribute substantially to AF risk estimation, but more research is needed.

Number of P-wave fragmentations on P-SAECG correlates with infiltrated atrial fat

BACKGROUND

Although atrial fibrillation (AF) triggers are known, the underlying AF substrate is less well understood. The goal of our study was to explore correlations between electrophysiological and structural characteristics of atria in patients with paroxysmal AF and individuals at AF risk.

METHODS

Patients in sinus rhythm (N = 90; age 57 ± 10 year; 55 men [63.2%]) with structural heart disease and paroxysmal AF (n = 12 [13%]), or with AF risk factors and LVEF > 35% (n = 78), underwent SAECG and cardiac magnetic resonance study. Interatrial and epicardial fat was analyzed with a Dark-blood DIR-prepared Fat-Water-separated sequence in the horizontal longitudinal axis. All local P-wave extrema were identified on SAECG leads during sinus rhythm. A P-wave fragmentation (Pf) was defined as an absolute difference between adjacent extrema which was above three standard deviations of noise, and was normalized by the duration of the P wave in the corresponding lead.

RESULTS

The Pf was greater on the filtered than on the unfiltered P-SAECG signal (13.1 ± 3.8 vs. 3.4 ± 1.2; P < 0.0001). Pf was the greatest on the Y lead (13.0 ± 3.5 on Y lead vs. 12.1 ± 3.4 on Z lead; P = 0.003. Pf on Z lead correlated with interatrial fat index (r = 0.544; P = 0.001). Epicardial fat significantly correlated with body mass index (BMI; r = 0.302; P = 0.015). After adjustment for BMI, left atrium (LA) size, epicardial fat, and interatrial septum width, interatrial fat independently associated with the Pf on Z lead (β-coefficient 0.009 [95%CI 0.0003-0.019]; P = 0.043).

CONCLUSIONS

Infiltrated atrial fat correlates with discontinuous conduction on posterior LA wall and represents AF early substrate.

In-silico modeling of atrial repolarization in normal and atrial fibrillation remodeled state

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and the total number of AF patients is constantly increasing. The mechanisms leading to and sustaining AF are not completely understood yet. Heterogeneities in atrial electrophysiology seem to play an important role in this context. Although some heterogeneities have been used in in-silico human atrial modeling studies, they have not been thoroughly investigated. In this study, the original electrophysiological (EP) models of Courtemanche et al., Nygren et al. and Maleckar et al. were adjusted to reproduce action potentials in 13 atrial regions. The parameter sets were validated against experimental action potential duration data and ECG data from patients with AV block. The use of the heterogeneous EP model led to a more synchronized repolarization sequence in a variety of 3D atrial anatomical models. Combination of the heterogeneous EP model with a model of persistent AF-remodeled electrophysiology led to a drastic change in cell electrophysiology. Simulated Ta-waves were significantly shorter under the remodeling. The heterogeneities in cell electrophysiology explain the previously observed Ta-wave effects. The results mark an important step toward the reliable simulation of the atrial repolarization sequence, give a deeper understanding of the mechanism of atrial repolarization and enable further clinical investigations.

P-wave morphology: underlying mechanisms and clinical implications

Increasing awareness of atrial fibrillation (AF) and its impact on public health revives interest in identification of noninvasive markers of predisposition to AF and ECG-based risk stratification. P-wave duration is generally accepted as the most reliable noninvasive marker of atrial conduction, and its prolongation has been associated with history of AF. However, patients with paroxysmal AF without structural heart disease may not have any impressive P-wave prolongation, thus suggesting that global conduction slowing is not an obligatory requirement for development of AF. P-wave morphology is therefore drawing increasing attention as it reflects the three-dimensional course of atrial depolarization propagation and detects local conduction disturbances. The factors that determine P-wave appearance include (1) the origin of the sinus rhythm that defines right atrial depolarization vector, (2) localization of left atrial breakthrough that defines left atrial depolarization vector, and (3) the shape and size of atrial chambers. However, it is often difficult to distinguish whether P-wave abnormalities are caused by atrial enlargement or interatrial conduction delay. Recent advances in endocardial mapping technologies have linked certain P-wave morphologies with interatrial conduction patterns and the function of major interatrial conduction routes. The value of P-wave morphology extends beyond cardiac arrhythmias associated with atrial conduction delay and can be used for prediction of clinical outcome of a wide range of cardiovascular disorders, including ischemic heart disease and congestive heart failure.