Correlation of P-wave properties with the size of left atrial low voltage areas in patients with atrial fibrillation

Predicting outcomes in persistent atrial fibrillation: the impact of surface ECG f-wave amplitude following pulmonary vein isolation

BACKGROUND

Fibrillatory wave amplitude (fWA) on 12-lead ECG predicts the outcome of ablation in atrial fibrillation (AF). We hypothesized that changes in fWA following wide circumferential isolation of pulmonary veins (WPVI) in persistent AF (peAF) is a better predictor of ablation outcome compared to baseline fWA.

METHODS

Eighty-nine patients (sustained peAF 7 ± 7 months) underwent a first-time WPVI. Sixty-second ECG signals devoid of QRST waves were recorded at baseline and at the end of the WPVI (endWPVI). fWA for each ECG lead and mean fWA (meanfWA) across the 12-lead ECG were computed. Patients with recurrence after the index WPVI underwent a redo to ensure complete PVI. The primary endpoint was long-term AF freedom OFF antiarrhythmics drugs (AADs) after one or two WPVI (SUCCESS group). The FAILURE group was defined as AF recurrence post-redo.

RESULTS

Over a mean follow-up of 35 ± 10 months, freedom from AF OFF AADs was achieved in 61% (SUCCESS group), while 29% had AF recurrence after redo WPVI (FAILURE group). The SUCCESS group showed significantly higher fWA values in ECG leads V1, V4, and V5 at baseline (p < 0.05), as well as in leads III, aVL, aVF, and V4, and in meanfWA at endWPVI (p < 0.05) compared to the FAILURE group. A baseline mean fWA ≥ 0.044 mV or a decrease in mean fWA ≤ 11% following WPVI predicted long-term sinus rhythm restoration with a sensitivity of 81% and a specificity of 69% (p < 0.05).

CONCLUSION

Low fWA values and a significant reduction in fWA following WPVI are associated with a high risk of AF recurrence in patients with peAF.

Value of P-wave Parameters in Predicting Outcomes of Repeat Catheter Ablation for Paroxysmal Atrial Fibrillation

BACKGROUND

Pulmonary vein isolation (PVI) has been established as an effective management option for symptomatic paroxysmal atrial fibrillation (PAF). We aimed to explore the role of P-wave parameters in a 12-lead electrocardiogram (ECG) in predicting the success of repeat PAF ablation.

METHODS

We enrolled consecutive patients who underwent a second AF ablation procedure for PAF in a UK tertiary center after an index ablation conducted between 2018 and 2019 and a repeat ablation up to 2021. A digital 12-lead ECG was recorded with a 1-50-Hz bandpass filter applied. P-wave duration (PWD), P-wave voltage (PWV), P-wave dispersion (PWDisp), and P-wave terminal force in V1 (PTFV1) were measured before and after the procedure. Changes were correlated with the 12-month clinical outcome. Procedural success was freedom from ECG-documented AF up to 12 months following ablation.

RESULTS

Study criteria were satisfied by 72 patients, of which 43 (60%) had successful repeat PVI at 12 months. The mean age is 65, and 47 (65%) were males. The demographics were comparable between both study arms. PWD decreased after successful repeat ablations (136.7 to 124.6 ms, p = 0.01) and failed repeat ablations (135.4 to 125.3 ms, p = 0.009) without a significant change between both arms. PMV and PWDisp did not change significantly after both study arms. PTFV1 significantly decreased after successful repeat ablations (-3.1 to -4.4 mm.s, p = 0.005) without a significant change after failed ablations (-2.9 to -2.7 mm.s, p = 0.42). Changes were statistically significant between both arms (p = 0.004).

CONCLUSION

PTFV1 reduction following the second AF ablation was correlated with successful repeat AF ablation at 12 months.

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach

BACKGROUND AND PURPOSE

Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage areas (LVAs) which are associated with poor ablation outcomes and increased thromboembolic risk. This study aims to evaluate the efficacy of a deep learning model applied to 12‑lead ECGs for non-invasively predicting the presence of LVAs, potentially guiding pre-ablation strategies and improving patient outcomes.

METHODS

A retrospective analysis was conducted on 204 AF patients, who underwent catheter ablation. Pre-procedural sinus rhythm ECGs and electroanatomical maps (EAM) were utilized alongside demographic data to train a deep learning model combining Long Short-Term Memory networks and Convolutional Neural Networks with a cross-attention layer. Model performance was evaluated using a 5-fold cross-validation strategy.

RESULTS

The model effectively identified the presence of LVA on the examined atrial walls, achieving accuracies of 78 % for both the anterior and posterior walls, and 82 % for the LA roof. Moreover, it accurately predicted the global left atrial (LA) average voltage <0.7 mV, with an accuracy of 88 %.

CONCLUSION

The study showcases the potential of deep learning applied to 12‑lead ECGs to effectively predict regional LVAs and global LA voltage in AF patients non-invasively. This model offers a promising tool for the pre-ablation assessment of atrial substrate, facilitating personalized therapeutic strategies and potentially enhancing ablation success rates.

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

P-Wave Duration/Amplitude Ratio Quantifies Atrial Low-Voltage Area and Predicts Atrial Arrhythmia Recurrence After Pulmonary Vein Isolation

BACKGROUND

Atrial low-voltage areas (LVAs) in patients with atrial fibrillation increase the risk of atrial arrhythmia (AA) recurrence after pulmonary vein isolation (PVI). Contemporary LVA prediction scores (DR-FLASH, APPLE) do not include P-wave metrics. We aimed to evaluate the utility of P-wave duration/amplitude ratio (PWR) in quantifying LVA and predicting AA recurrence after PVI.

METHODS

In 65 patients undergoing first-time PVI, 12-lead ECGs were recorded during sinus rhythm. PWR was calculated as the ratio between the longest P-wave duration and P-wave amplitude in lead I. High-resolution biatrial voltage maps were collected and LVAs included bipolar electrogram amplitudes < 0.5 mV or < 1.0 mV. An LVA quantification model was created with the use of clinical variables and PWR, and then validated in a separate cohort of 24 patients. Seventy-eight patients were followed for 12 months to evaluate AA recurrence.

RESULTS

PWR strongly correlated with left atrial (LA) (< 0.5 mV: r = 0.60; < 1.0 mV: r = 0.68; P < 0.001) and biatrial LVA (< 0.5 mV: r = 0.63; < 1.0 mV: r = 0.70; P < 0.001). Addition of PWR to clinical variables improved model quantification of LA LVA at the < 0.5 mV (adjusted R2 = 0.59 to 0.68) and < 1.0 mV (adjusted R2 = 0.59 to 0.74) cutoffs. In the validation cohort, PWR model-predicted LVA correlated strongly with measured LVA (< 0.5 mV: r = 0.78; < 1.0 mV: r = 0.81; P < 0.001). PWR model was superior to DR-FLASH (area under the receiver operating characteristic curve [AUC] 0.90 vs 0.78; P = 0.030) and APPLE (AUC 0.90 vs 0.67; P = 0.003) at detecting LA LVA and similar at predicting AA recurrence after PVI (AUC 0.67 vs 0.65 and 0.60).

CONCLUSION

Our novel PWR model accurately quantifies LVA and predicts AA recurrence after PVI. PWR model-predicted LVA may help guide patient selection for PVI.

P-wave duration as a marker of atrial remodeling in patients referred to ablation for atrial fibrillation: A new stratification tool emerging?

Ablation of atrial fibrillation is one of the most widely applied invasive procedures in cardiovascular medicine, and populations with atrial fibrillation continuously rise. Recurrence rates are, however, consistently high, even in patients without severe comorbidities. Robust stratification algorithms to distinguish patients suitable for ablation are generally lacking. This is a fact caused by the inability to incorporate evidence of atrial remodeling and fibrosis, e.g., atrial remodeling, in the decision pathways. Cardiac magnetic resonance is a powerful tool in identifying fibrosis; however, it is costly and not routinely used. Electrocardiography has been generally underutilized in clinical practice during pre-ablative screening. One of the characteristics of the electrocardiogram that can give us valuable data depicting the existence and the extent of atrial remodeling and fibrosis is the duration of the P-wave. Currently, many studies support the implementation of P-wave duration in the routine practice of patient evaluation as a surrogate marker of existing atrial remodeling, that in turn predicts recurrence after ablation of atrial fibrillation. Further research is guaranteed to establish this electrocardiographic characteristic in our stratification quiver.

Validation of diagnostic accuracy of a handheld, smartphone-based rhythm recording device

AIM

Evaluation of the diagnostic accuracy of a rhythm recording device, for detection of atrial tachyarrhythmia (ATA) and atrial fibrillation (AF) compared to 12-lead-electrocardiogram (12-L-ECG).

RESEARCH DESIGN AND METHODS

Two hundred 12-L-ECGs (reference standard) and Coala Heart Monitor (CHM) recordings (index test) were collected from 189 patients. Two electrophysiologists independently performed manual analysis of all 12-L-ECGs and CHM recordings in random order. The CHM recordings were also analyzed by an automatic algorithm and compared to the results of the reference standard.

RESULTS

Manual analysis of CHM for ATA showed a sensitivity of 98.9% (95% confidence interval (CI): 94.0-100) and a specificity of 100% (CI: 96.6-100). Manual analysis for AF had a sensitivity of 100% (CI: 95.3-100) and a specificity of 97.5% (CI: 93.0-99.5). Automatic analysis for ATA showed a sensitivity of 93.5% (CI: 86.3-97.6) and a specificity of 92.6% (CI: 85.9-96.7). Automatic analysis for AF showed a sensitivity of 97.4% (CI: 91.0-99.7) and a specificity of 86.1% (CI: 78.6-91.7).

CONCLUSION

CHM has a very high accuracy for ATA and AF in manual analysis and a high accuracy for ATA and AF in automatic analysis, making the device suitable for screening.

A systematic review and meta-analysis of the safety and efficacy of left atrial substrate modification in atrial fibrillation patients with low voltage areas

Background

The left atrial low-voltage areas (LVAs) are associated with atrial fibrosis; however, it is not clear how the left atrial LVAs affect the recurrence of arrhythmias after catheter ablation, and the efficacy and safety of the left atrial substrate modification based on LVAs as a strategy for catheter ablation of atrial fibrillation (AF) are not evident for AF patients with LVAs.

Methods

We performed a systematic search to compare the arrhythmia recurrence in AF patients with and without LVAs after conventional ablation and arrhythmia recurrence in LVAs patients after conventional ablation with and without substrate modification based on LVAs.

Result

A total of 6 studies were included, involving 1,175 patients. The arrhythmia recurrence was higher in LVA patients after conventional ablation (OR: 5.14, 95% CI: [3.11, 8.49]; P < 0.00001). Additional LVAs substrate modification could improve the freedom of arrhythmia in LVAs patients after the first procedure (OR: 0.30, 95% CI: [0.15, 0.62]; P = 0.0009). However, there was no significant difference after multiple procedures (P = 0.19). The procedure time (MD: 26.61, 95% CI [15.79, 37.42]; P < 0.00001) and fluoroscopy time (MD: 6.90, 95% CI [4.34, 9.47]; P < 0.00001) in LVAs patients with additional LVAs substrate modification were significantly increased compared to LVAs patients' without substrate modification. Nevertheless, there were no higher LVAs substrate modification-related complications (P = 0.93) between LVAs patients with and without additional LVAs substrate modification. In the subgroup analysis, the additional LVAs substrate modification reduced the risk of arrhythmia recurrence in LVAs patients during the follow-up time, which was 12 months (OR: 0.32, 95% CI (0.17, 0.58); P = 0.002), and box isolation (OR: 0.37, 95% CI (0.20, 0.69); P = 0.002) subgroups, but the type of AF, follow up >12 months and homogenization subgroups were not statistically significant. Trial sequential analysis shows conclusive evidence for the LVAs ablation.

Conclusion

This study has shown that LVAs could improve the risk of arrhythmia recurrence in AF patients after conventional ablation. And additional LVAs substrate modification after conventional ablation could increase the freedom of arrhythmia recurrence in LVAs patients. Interestingly, the box isolation approach appeared more promising.

Systematic review registration

[http://www.crd.york.ac.uk/prospero], identifier [CRD42021239277].

Fibrillatory Wave Amplitude Evolution during Persistent Atrial Fibrillation Ablation: Implications for Atrial Substrate and Fibrillation Complexity Assessment

Background. Fibrillatory Wave Amplitude (FWA) has been described as a non-invasive marker of atrial fibrillation (AF) complexity, and it predicts catheter ablation outcome. However, the actual determinants of FWA remain incompletely understood. Objective. To assess the respective implications of anatomical atrial substrate and AF spectral characteristics for FWA. Methods. Persistent AF patients undergoing radiofrequency catheter ablation were included. FWA was measured on 1-min ECG by TQ concatenation in Lead I, V1, V2, and V5 at baseline and immediately before AF termination. FWA evolution during ablation was compared to that of AF dominant frequency (DF) measured by Independent Component Analysis on 12-lead ECG. FWA was compared to the extent of endocardial low-voltage areas (LVA I < 10%; II 10–20%; III 20–30%; IV > 30%), to the surface of healthy left atrial tissue, and to P-wave amplitude in sinus rhythm. The predictive value of FWA for AF recurrence during follow-up was assessed. Results. We included 29 patients. FWA remained stable along ablation procedure with comparable values at baseline and before AF termination (Lead I p = 0.54; V1 p = 0.858; V2 p = 0.215; V5 p = 0.14), whereas DF significantly decreased (5.67 ± 0.68 vs. 4.95 ± 0.58 Hz, p < 0.001). FWA was higher in LVA-I than in LVA-II, -III, and -IV in Lead I and V5 (p = 0.02 and p = 0.01). FWA in V5 was strongly correlated with the surface of healthy left atrial tissue (R = 0.786; p < 0.001). FWA showed moderate to strong correlation to P-wave amplitude in all leads. Finally, FWA did not predict AF recurrence after a follow-up of 23.3 ± 9.8 months. Conclusions. These findings suggest that FWA is unrelated to AF complexity but is mainly determined by the amount of viable atrial myocytes. Therefore, FWA should only be referred as a marker of atrial tissue pathology.

Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference

Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.

Beat-to-Beat P-Wave Analysis Outperforms Conventional P-Wave Indices in Identifying Patients with a History of Paroxysmal Atrial Fibrillation during Sinus Rhythm

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers-one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754-0.917) vs. SPWIs AUC: 0.721 (0.613-0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients.

Reduction of mapping time in pulmonary vein isolation using atrial pacing during left atrial voltage map acquisition

INTRODUCTION

3D mapping systems are used during radiofrequency (RF) pulmonary vein isolation (PVI) to facilitate catheter navigation and to provide additional electroanatomical information as a surrogate marker for the presence and location of fibrotic atrial myocardium. Electric voltage information can only be measured when the myocardium is depolarized. Low heart rates or frequent premature atrial beats can significantly prolong creation of detailed left atrial voltage maps. This study was designed to evaluate the potential advantage of voltage information collection during atrial pacing instead of acquisition during sinus rhythm.

METHODS AND RESULTS

A total of 40 patients were included in the study, in 20 consecutive patients voltage mapping was performed during sinus rhythm, and in the following 20 patients during atrial pacing. The average age of the included patients was 69.5 ± 9.4, 17 of 40 patients (43%) were male. All procedures were performed using the Carto 3D Mapping system. For LA voltage mapping, a multipolar circular mapping catheter was used. The atrium was paced via the proximal coronary sinus catheter electrodes with a fixed cycle length of 600 ms. By mapping during atrial pacing mapping time was reduced by 35% (441 s. (±141) vs. 683 s. (±203) p = 0.029) while a higher number of total mapping points were acquired (908 ± 560 vs. 581 ± 150, p = 0.008).

CONCLUSION

Acquiring left atrial low voltage maps during atrial pacing significantly reduces mapping time. As pacing also improves comparability of left atrial electroanatomical maps we suggest that this approach may be considered as a standard during these procedures.

Effects of occlusal disharmony on susceptibility to atrial fibrillation in mice

Tooth loss or incorrect positioning causes occlusal disharmony. Furthermore, tooth loss and atrial fibrillation (AF) are both risk factors for ischemic stroke and coronary heart disease. Therefore, we hypothesized that occlusal disharmony-induced stress increases susceptibility to AF, and we designed the present study to test this idea in mice. Bite-opening (BO) was done by cementing a suitable appliance onto the mandibular incisor to cause occlusal disharmony by increasing the vertical height of occlusion by 0.7 mm for a period of 2 weeks. AF susceptibility, evaluated in terms of the duration of AF induced by transesophageal burst pacing, was significantly increased concomitantly with atrial remodeling, including fibrosis, myocyte apoptosis and oxidative DNA damage, in BO mice. The BO-induced atrial remodeling was associated with increased calmodulin kinase II-mediated ryanodine receptor 2 phosphorylation on serine 2814, as well as inhibition of Akt phosphorylation. However, co-treatment with propranolol, a non-selective β-blocker, ameliorated these changes in BO mice. These data suggest that improvement of occlusal disharmony by means of orthodontic treatment might be helpful in the treatment or prevention of AF.

P-wave vector magnitude predicts the left atrial low-voltage area in patients with paroxysmal atrial fibrillation

BACKGROUND

P-wave amplitude (PWA) parameters can be the surrogate measures of the left atrial low-voltage areas (LVAs).

METHODS

We measured PWAs using an automated system in 50 patients with paroxysmal atrial fibrillation (AF). We examined the relationships between left atrial LVAs and PWA parameters, including P-wave vector magnitude, calculated as the square root of the sum of lead II PWA squared, lead V6 PWA squared, and a one-half lead V2 PWA squared.

RESULTS

Lead I PWA was most strongly correlated with LVAs in the anterior wall and appendage (anterior wall, R = -0.391, P = 0.006; appendage, R = -0.342, P = 0.016), whereas lead II PWA was most strongly correlated with LVAs in the septum, posterior wall, and bottom wall (septum, R = -0.413, P = 0.003; posterior wall, R = -0.297, P = 0.039; bottom wall; R = -0.288, P = 0.045). Although maximum, minimum, mean, and lead I PWAs were not correlated with total LVA, P-wave vector magnitude and lead II PWA were significantly correlated with total LVA (P-wave vector magnitude, R = -0.430, P = 0.002; lead II PWA, R = -0.323, P = 0.023). P-wave vector magnitude achieved the highest accuracy for predicting significant LVA (total LVA > 10%) with an area under the curve of 0.772; sensitivity, specificity, and positive and negative predictive values were 64%, 88%, 85%, and 69%, respectively, for the cutoff value of 0.130 mV.

CONCLUSION

P-wave vector magnitude is a useful electrocardiographic predictor of left atrial LVAs.