High-resolution signal-averaged analysis of atrial electromagnetic characteristics in patients with paroxysmal lone atrial fibrillation

P-wave peak time and P-wave dispersion in surface electrocardiography as initial predictors of new-onset atrial fibrillation in early-onset hypertension

Atrial fibrillation (AF) is common in hypertension, and electrophysiological remodelling may contribute to the early stage of the disease. This study aimed to develop electrocardiography (ECG) prediction models on new-onset AF (NAF) in early-onset hypertension (e-HTN). This matched case-control study included primary hypertension patients with onset <5 years defined as e-HTN and without documented AF. Developed NAF was the risk group and non-developed NAF was control group with 1:2 ratio. Group was matched according to age, gender, follow-up time, and duration of hypertension. Parameters of ECG and echocardiography between the groups at the baseline and end of follow-up will be compared. A total of 348 e-HTN with 116 developed NAF during follow-up (60.2 ± 14.5 months) were included. At baseline ECG, duration of QRS (100.84 ms ± 15.69 ms vs 94.80 ms ± 15.68 ms), Pmax (106.75 ms ± 7.93 ms vs 101.77 ms ± 6.78 ms), Pmin (70.24 ms ± 5.59 ms vs 68.17 ms ± 5.61 ms), P-wave dispersion (PD) (36.50 ms ± 5.25 ms vs 33.60 ms ± 5.46 ms), P-wave Peak Time (PWPT) II (62.01 ms ± 3.92 ms vs 54.29 ms ± 6.73 ms), and PWPT V1 (55.31 ms ± 2.89 ms vs 51.24 ms ± 4.05 ms) were significantly higher in developed NAF (all P-value < 0.05). LVMI was also significantly higher in bivariate analysis, but only Pmax, Pmin, PD, PWPT, non-RAAS inhibitor, and uncontrolled hypertension were independently associated with developed NAF. Baseline PWPT II with cut-off ≥57.9 ms and PD ≥ 35.5 ms has high sensitivity and specificity on NAF prediction. In conclusion, baseline PWPT and PD are potential electrophysiological parameters for predicting NAF in e-HTN.

Impact of surgical weight loss on novel P-wave-related variables which are nominated as predictors of atrial arrhythmias

BACKGROUND

Bariatric surgery has been associated with reduced cardiovascular mortality and morbidity in obese patients. In this study, we aimed to evaluate the alterations of novel P-wave related atrial arrhythmia predictors in patients who achieved effective weight loss with bariatric surgery.

METHODS

The study included 58 patients who underwent bariatric surgery. We measured heart rate, PR, P wave (PW) max, PW min, Average P axis, P wave peak time (PWPT) in lead D2 and lead V1, terminal force in lead V1 (V1TF), and we estimated P wave dispersion (PWdis) interval both pre-operation and 6 months after operation.

RESULTS

Heart rate, PR, PW max, PW min, PWdis, Average P axis, PWPTD2, PWPTV1 and V1TF values, which were close to the upper limit in the pre-op period, showed statistically significant decreases at 6 months after the operation. The most prominent changes were observed in PW dis (51.15 ± 9.70 ms vs. 48.79 ± 9.50 ms, p = .010), PWPTD2 (55.75 ± 6.91 ms vs. 50.59 ± 7.67 ms, p < .001), PWPTV1 (54.10 ± 7.06 ms vs. 48.05 ± 7.64 ms, p < .001) and V1TF (25 [43.1%] vs. 12 [20.7%], p < .001).

CONCLUSIONS

The results of our study indicated that bariatric surgery has positive effects on the regression of ECG parameters which are predictors of atrial arrhythmias, particularly atrial fibrillation (AF).

Novel electrocardiography parameter for paroxysmal atrial fibrillation in acute ischaemic stroke patients: P wave peak time

Introduction

In this study, we aimed to investigate the usefulness of P wave peak time (PWPT), a novel ECG parameter, in patients who were diagnosed with acute ischaemic stroke (IS) and had paroxysmal atrial fibrillation (PAF) on Holter monitoring.

Materials and methods

In this retrospective cohort study, we included 90 consecutive patients with acute IS who were admitted to our hospital between January 2017 and July 2019. PWPT was described as the time from the beginning of the P wave to its peak, and it was measured from leads DII and VI. The PAF diagnosis was confirmed if it was detected on the ECG during palpitation or in rhythm during the Holter recordings.

Results

In this study, 34 (37.7%) patients with acute IS were diagnosed with PAF. In multivariate analyses, the independent predictors of PAF were age, PWPT in lead VI and PWPT in lead DII (OR: 1.34, 95% CI 1.15 to 1.56; p<0.001). A receiver operating curve analysis demonstrated that area under the curve values for PWPT in lead DII for PAF were 0.88 (95% CI 0.81 to 0.95, p<0.001). The cut-off value for PWPT in lead DII in predicting PAF was 68.5 ms with a sensitivity of 82.4% and a specificity of 75.0%.

Conclusion

To the best of our knowledge, this is the first study to demonstrate a significant relationship between PWPT in lead DII and PAF among patients with acute IS.

Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation

BACKGROUND AND OBJECTIVES

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects.

METHODS

Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3-5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset.

RESULTS

In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction.

CONCLUSION

The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria.

Electrophysiological abnormalities in patients with paroxysmal atrial fibrillation in the absence of overt structural heart disease

Purpose

The aim of the present study was to define the atrial electrical substrate in patients with paroxysmal atrial fibrillation (AF) occurring in the absence of overt structural heart disease and to assess if electrophysiological parameters could predict AF recurrence after radiofrequency ablation in this population.

Methods and results

45 consecutive patients (39 male, age 59 ± 10 years) with paroxysmal AF and without overt structural heart disease, referred for radiofrequency catheter ablation, were prospectively enrolled. A cohort of 12 age-matched patients without a history of AF, served as a control group. Atrial electrical substrate was assessed by P-wave signal-averaging, intracardiac conduction delays and refractory periods. Total P wave duration during signal-averaging was longer in patients with paroxysmal AF than in controls (140 ± 19 ms vs 123 ± 13 ms, p = 0.004). Patients with paroxysmal AF showed an increase in right intra-atrial (40.2 ± 11.3 ms vs 31.7 ± 11.8 ms, p = 0.02) and inter-atrial conduction delays (87.93 ± 22.0 ms vs 65.3 ± 15.6 ms, p = 0.001) in sinus rhythm. Refractory periods in the right atrium were longer in patients with paroxysmal AF (265 ± 44 ms vs 222 ± 32 ms, p = 0.002). After ablation, 22 patients had AF recurrence but showed no differences in electrophysiological parameters compared to patients without recurrence.

Conclusion

Electrophysiological abnormalities are present in patients with paroxysmal AF without overt structural heart disease. Neither signal-averaged P-wave duration nor intracardiac atrial electrophysiology could predict arrhythmia recurrence after pulmonary vein isolation.

Lone Atrial Fibrillation: Risk Factors, Triggers And Ablation Techniques

BACKGROUND

Since its introduction in 1953, lone atrial fibrillation (LAF) has not been defined with any consistency, resulting in an enormous variation in the way the term is used. Inherent to this, results from studies vary considerably. Many predisposing factors and pathogenic influences have been discovered over the past years, which raise the question if the term LAF should still be used and if the treatment should be different from non-lone atrial fibrillation (non-LAF). Therefore this systematic review on LAF provides an overview of risk factors and triggers, the second part focuses on the application of catheter and surgical ablation techniques.

METHODS

A systematic literature search was performed in the PubMed database. All identified articles were screened and checked for eligibility by the two authors. Additional literature was sought by screening the references of eligible articles.

CONCLUSIONS

Insufficient evidence exists to consider LAF as a real, isolated and useful entity. A re-definition or even avoiding the use of the term LAF might be appropriate.

Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation

A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients.

The P Wave Time-Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation

BACKGROUND

The study of atrial conduction defects associated with the onset of paroxysmal atrial fibrillation (PAF) can be addressed by analyzing the P wave from the surface electrocardiogram (ECG). Traditionally, signal-averaged ECGs have been mostly used for this purpose. However, this alternative hinders the possibility to quantify every single P wave, its variability over time, as well as to obtain complimentary and evolving information about the arrhythmia. This work analyzes the time progression of several time and frequency P wave features as potential indicators of atrial conduction variability several hours preceding the onset of PAF.

METHODS

The longest sinus rhythm interval from 24-hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1-hour periods. Every single P wave was automatically delineated and characterized by 16 time and frequency metrics, such as its duration, absolute energy in several frequency bands and high-to-low-frequency energy ratios. Finally, the P wave variability over each 1-hour period was estimated from the 16 features making use of a least-squares linear fitting. As a reference, the same parameters were also estimated from a set of 1-hour ECG segments randomly chosen from a control group of 53 healthy subjects age-, gender-, and heart rate-matched.

RESULTS

All the analyzed metrics provided an increasing P wave variability trend as the onset of PAF approximated, being P wave duration and P wave high-frequency energy the most significant individual metrics. The linear fitting slope α associated with P wave duration was (2.48 ± 1.98)×10(-2) for healthy subjects, (23.8 ± 14.1)×10(-2) for ECG segments far from PAF and for (81.8 ± 48.7)×10(-2) ECG segments close to PAF p = 6.96×10(-22) . Similarly, the P wave high-frequency energy linear fitting slope was (2.42 ± 4.97)×10(-9) , (54.2 ± 107.1)×10(-9) and (274.2 ± 566.1)×10(-9) , respectively (p = 2.85×10(-20) ). A univariate discriminant analysis provided that both P wave duration and P wave high-frequency energy could discern among the three ECG sets with diagnostic ability around 80%, which was improved up to 88% by combining these metrics in a multivariate discriminant analysis.

CONCLUSION

Alterations in atrial conduction can be successfully quantified several hours before the onset of PAF by estimating variability over time of several time and frequency P wave features.

Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation

This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave. In order to validate the algorithm, electrocardiogram segments 2 h preceding the onset of PAF episodes from 46 different patients were assessed. According to the expected intermittently disturbed atrial conduction before the onset of PAF, all the analyzed Gaussian metrics showed an increasing variability trend as the PAF onset approximated. Moreover, the Gaussian P-wave width reported a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF, and patients less than 1 h close to a PAF episode. This discriminant power was similar to those provided by the most classical time-domain approach, i.e., the P-wave duration. However, this newly proposed parameter presents the advantage of being less sensitive to a precise delineation of the P-wave boundaries. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 86.69%. In conclusion, morphological P-wave characterization provides additional information to the metrics based on P-wave timing.

P-wave duration in lead aVR and the risk of atrial fibrillation in hypertension

BACKGROUND

Hypertension entails atrial remodeling that affect P-wave (PW) duration on electrocardiogram (ECG). PW indices (e.g., variance, dispersion, and terminal force) are associated with a higher risk for atrial fibrillation (AF), but their calculation requires multiple measurements of PW duration, limiting their use in clinical practice. We evaluated whether PW duration in specific ECG leads may identify patients with increased susceptibility to AF in a population of hypertensive patients.

METHODS

In a case-control study, AF and control subjects were matched for age, sex, and left atrial (LA) dimensions. PW duration was measured from digitally stored ECGs. Logistic regression was used to assess the association of PW duration and indices with AF.

RESULTS

We enrolled 44 hypertensive AF patients (16 paroxysmal and 28 persistent) and 44 hypertensive controls. AF and control subjects were matched for sex (males, n = 27), age (67 ± 8 years), LA diameter (40 ± 5 mm), and were comparable for left ventricular mass (45 ± 11 g/m(2.7) vs 48 ± 12 g/m(2.7) , P = 0.19), ejection fraction (58 ± 7% in both groups), and prevalence of mild valvular heart disease (7% vs 5%; P = 0.64). PW duration in lead aVR was significantly higher in AF patients as compared with controls (115 ± 18 ms vs 101 ± 14 ms; P < 0.0001) and was the best independent predictor of AF in multivariable logistic regression (PW ≥ 100 ms: RR = 3.7; 95% CI: 1.3-10.3; P = 0.02).

CONCLUSIONS

Simple measurement of PW duration in lead aVR allows effective identification of AF patients in a population of hypertensives. Confirmation of this finding in a larger population would provide a simple and effective risk marker of AF in hypertensive patients.

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.