Atrial fibrillation progression: How sick is the atrium?

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.

Risk Factors for the Development of Atrial Fibrillation in the Kazakh Patients

BACKGROUND: Atrial fibrillation (AF) is the most common persistent heart rhythm disorder. AIM: Assessment of clinical predictors of atrial fibrillation in the Kazakh population. METHODS: An analytical clinical and epidemiological study of 75 patients with AF of Kazakh nationality. Descriptive analysis of medical records was carried out and the results of laboratory and instrumental research methods. Statistical analysis was carried out using the Statistica 6.0 Software package from StatSoft Inc. (USA) and MS Excel. RESULTS: In the majority of the studied patients (86.7%), atrial fibrillation was associated with hypertension, in 49.3% of patients was diagnosed with coronary artery disease. A permanent form of atrial fibrillation was observed in 63%, in 20% AF manifested itself in the form of paroxysms, in 17% AF was persistent. AF, which arose against the background of CHF, was established in 41.3% of the patients studied by us, while a decrease in LVEF below 40% was observed in 21.3% of the examined patients. In our study, diabetes mellitus was diagnosed as a comorbid pathology in 24% of patients and diabetes mellitus correlated with permanent AF in 66.7%. Thyroid pathology was observed in 9.3% cases. CRHD as a concomitant disease occurred in 12% of cases. In 5.7% of cases, AF was registered as idiopathic (primary) without a history of cardiovascular and concomitant pathology. Smoking as a risk factor was observed in 16%, alcohol consumption, was noted by 8 patients (10.7%). In 40% of cases, patients with AF were obese, 45.3% of the patients were overweight (BMI ≥25).

Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation

OBJECTIVE

The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice.

METHODS

History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data.

RESULTS

Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden.

CONCLUSION

Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.

Persistent atrial fibrillation vs paroxysmal atrial fibrillation: differences in management

INTRODUCTION

Atrial fibrillation (AF) is the most common human arrhythmia. AF is a progressive disease, initially being nonsustained and induced by trigger activity, and progressing towards persistent AF through alteration of the atrial myocardial substrate. Treatment of AF aims to decrease the risk of stroke and improve the quality of life, by preventing recurrences (rhythm control) or controlling the heart rate during AF (rate control). In the last 20 years, catheter-based and, less frequently, surgical and hybrid ablation techniques have proven more successful compared with drug therapy in achieving rhythm control in patients with AF. However, the efficiency of ablation techniques varies greatly, being highest in paroxysmal and lowest in long-term persistent AF. Areas covered: In this review, we discuss the fundamental differences between paroxysmal and persistent AF and the potential impact of those differences on patient management, emphasizing the available therapeutic strategies to achieve rhythm control. Expert commentary: Treatment to prevent AF recurrences is suboptimal, particularly in patients with persistent AF. Emerging technologies, such as documentation of atrial fibrosis using magnetic resonance imaging and documentation of electrical substrate using advanced electrocardiographic imaging techniques are likely to provide valuable insights about patient-specific tailoring of treatments.