Association of beat-to-beat P-wave analysis index to the extent of left atrial low-voltage areas in patients with paroxysmal atrial fibrillation.

BACKGROUND: Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping. METHODS: Patients with paroxysmal AF scheduled for pulmonary vein isolation, without evident structural remodeling, were included. Pre-ablation electroanatomical voltage maps were used to calculate the surface of LVAs (<0.5 mV). B2B index was compared between patients with small versus large LVAs. RESULTS: 35 patients were included (87% male, median age 62). The median surface area of LVAs was 7.7 (4.4-15.8) cm2 corresponding to 5.6 (3.3-12.1) % of LA endocardial surface. B2B index was 0.57 (0.52-0.59) in patients with small LVAs (below the median) compared to 0.65 (0.56-0.77) in those with large LVAs (above the median) (p=0.009). In the receiver operator characteristic curve analysis for predicting large LVAs, the c-statistic was 0.75 (p=0.006) for B2B index and 0.81 for the multivariable model including B2B index (multivariable p=0.04) and P-wave duration. CONCLUSION: In patients with paroxysmal AF without overt atrial myopathy, B2B P-wave analysis appears to be a useful non-invasive correlate of low-voltage areas-and thus fibrosis-in the LA. This finding establishes a pathophysiological basis for B2B index and its potential usefulness in the selection process of patients who are likely to benefit most from further invasive treatment.

P-wave Indices as Predictors of Atrial Fibrillation: The Lion from a Claw.

The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical-and potentially structural-properties. However, technical and biological reasons, including-but not limited to-the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF. More sophisticated methods of analyzing the ECG signal, on a single- or multi- beat basis, along with novel approaches to data handling, namely machine learning, seem to be leading up to more accurate and robust ways to obtain clinically useful information from the humble P wave.

Pattern of Facebook use by university students during the COVID-19 pandemic: relations with loneliness and resilience.

Considering young adults' extensive use of social media since the outbreak of the COVID-19 pandemic, the present study examined the pattern of Facebook use by university students during the period of hygienic crisis. Specifically, it was investigated students' Facebook intensity use and self-disclosure to unknown online friends, as well as the role of sense of resilience and loneliness in the manifestation of the above Facebook behaviors. Overall, 792 undergraduate and postgraduate university students (48% women) completed online self-report questionnaires regarding the above variables. Undergraduate students, regardless of gender and Department of studies, made more intense Facebook use and self-disclosure to unknown online friends. Sense of loneliness positively predicted students' online self-disclosure not only directly but also indirectly through their Facebook intensity use. Students' resilience negatively moderated the relationship between sense of loneliness and Facebook behaviors. The findings propose a new explanatory model of emotional and behavioral mechanisms, which leads to a less safe pattern of Facebook use. This pattern possibly reflects youth's collective tendency to use this social media platform recklessly as a way out of crisis periods, such as the pandemic period. The emergence of this pattern could be useful for launching or enriching university counselling/prevention actions aimed at strengthening students' psycho-emotional skills, and subsequently their prudent use of social media.

The anti-inflammatory effects of aerobic exercise training in patients with type 2 diabetes: A systematic review and meta-analysis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a low-grade, chronic inflammatory disease, associated with increased cardiovascular risk. The purpose of this systematic review/ meta-analysis was to evaluate the effects of aerobic exercise training (AET) on inflammatory markers in T2DM patients. METHODS: The literature search was conducted utilizing PubMed, Web of Science, Embase, and the Cochrane Library from their inception up to April 2022. We screened only for randomized controlled trials (RCTs) investigating the effects of AET on C-reactive protein (CRP) and adipokines: adiponectin, resistin, interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-a), along with changes in anthropometric indices and glycemic control in adult T2DM patients. Pooled post-exercise weighted mean differences (WMDs) with 95% Confidence Intervals (CIs) were calculated for all outcomes of interest between exercise-treated patients and controls. RESULTS: Twenty-six RCTs involving 1239 T2DM patients were retrieved from the databases for meta-analysis. The cumulative results showed that post-AET inflammatory markers were lower in exercise-treated patients compared to controls regarding CRP (mg/L): WMD: -0.91; 95%CIs: -1.43, -0.40; p < 0.001 resistin (mg/ml): (WMD: -2.08; 95%CIs: -3.32, -0.84; p < 0.001); TNF-a (pg/ml): (WMD: -2.70; 95%CIs: -4.26, -1.14; p < 0.001), and IL-6 (pg/ml): (WMD: -1.05; 95%CIs: -1.68, -0.43; p < 0.001). Those effects were accompanied by significant amelioration of fasting glucose (mg/dl) (WMD: -13.02; 95%CIs: -25.39, -0.66; p = 0.04), HbA1c (%) (WMD: -0.51; 95%CIs: -0.73, -0.28, p < 0.001), and fat mass (%) (WMD: -3.14; 95%CI: -4.71, -1.58; p < 0.001). Our meta-analysis demonstrated less-consistent results for adiponectin (µg/ml), (WMD: 1.00; 95%CI: -0.12, 2.12; p = 0.08) and body-mass index (kg/m2) (WMD: -1.34; 95%CI: -2.76, 0.08; p = 0.06) tending to differ between AET and control group. CONCLUSIONS: AET can significantly reduce the inflammatory burden in T2DM patients. by ameliorating the circulating levels of CRP, resistin, TNF-a and IL-6, even without accompanied significant weight-loss. The clinical impact of those anti-inflammatory effects of AET needs to be determined.

P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation.

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24-4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA2DS2-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

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.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies.

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.