Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting millions of people worldwide. The cardiac autonomic nervous system (ANS) is widely recognized as playing a key role in both the initiation and propagation of AF. This paper reviews the background and development of a unique cardioneuroablation technique for the modulation of the cardiac ANS as a potential treatment for AF. The treatment uses pulsed electric field energy to selectively electroporate ANS structures on the epicardial surface of the heart. Insights from in vitro studies and electric field models are presented as well as data from both pre-clinical and early clinical studies.

https://www.mdpi.com/2308-3425/10/6/238/pdf?version=1685611160

Cardioneuroablation Using Epicardial Pulsed Field Ablation for the Treatment of Atrial Fibrillation

Atrial fibrillation (AF) is the most common clinical arrhythmia disorder. It can easily lead to complications such as thromboembolism, palpitations, dizziness, angina, heart failure, and stroke. The disability and mortality rates associated with AF are extremely high, significantly affecting the quality of life and work of patients. With the deepening of research into the brain-heart connection, the link between AF and stroke has become increasingly evident. AF is now categorized as either Known Atrial Fibrillation (KAF) or Atrial Fibrillation Detected After Stroke (AFDAS), with stroke as the baseline. This article, through a literature review, briefly summarizes the current pathogenesis of KAF and AFDAS, as well as the status of their clinical pharmacological and non-pharmacological treatments. It has been found that the existing treatments for KAF and AFDAS have limited efficacy and are often associated with significant adverse reactions and a risk of recurrence. Moreover, most drugs and treatment methods tend to focus on a single mechanism pathway. For example, drugs targeting ion channels primarily modulate ion channels and have relatively limited impact on other pathways. This limitation underscores the need to break away from the “one disease, one target, one drug/measurement” dogma for the development of innovative treatments, promoting both drug and non-drug therapies and significantly improving the quality of clinical treatment. With the increasing refinement of the overall mechanisms of KAF and AFDAS, a deeper exploration of physiological pathology, and comprehensive research on the brain-heart relationship, it is imperative to shift from long-term symptom management to more precise and optimized treatment methods that are effective for almost all patients. We anticipate that drugs or non-drug therapies targeting the central nervous system and upstream pathways can guide the simultaneous treatment of multiple downstream pathways in AF, thereby becoming a new breakthrough in AF treatment research.

Research on atrial fibrillation mechanisms and prediction of therapeutic prospects: focus on the autonomic nervous system upstream pathways

Background Atopic diseases and atrial fibrillation (AF) seem to share an underlying inflammatory pathology. To date, some population-based studies have explored the relationship between the two. We aimed to conduct a meta-analysis to examine the role of atopic condition in AF risk. Methods All relevant observational studies in PubMed and EMBASE databases up to November 2021 were searched. In RevMan 5.3, we used random-effects or fixed-effects models to pool the effect sizes of hazard ratio (HR), odds ratio (OR) and their corresponding 95% confidence intervals (95% CI). In addition, I2 and Cochran Q test were used to evaluate the heterogeneity. Results A total of 2488 records were retrieved. After screening according to the predetermined criteria, 6 cohort studies and 2 case-control studies were included in this meta-analysis. Herein, the meta-analysis of 6 cohort studies suggested that atopic diseases potentially increased the AF risk with the pooled HR of 1.26 (95%CI,1.14–1.39), while the pooled effect size (OR, 1.04; 95%CI,0.74–1.46) of 2 case-control studies was not statistically significant. Based on the types of atopic diseases, further subgroup analyses of 6 cohort studies revealed that asthma, allergic rhinitis, and atopic dermatitis all potentially increased the risk of subsequent AF with the pooled HR of 1.41 (n = 4; 95%CI, 1.25–1.58), 1.12 (n = 1; 95%CI,1.10–1.14) and 1.06 (n = 3; 95%CI, 1.01–1.12), respectively. Conclusion This meta-analysis demonstrated that patients with atopic diseases have a higher risk of developing AF, particularly those with asthma.

Association of atopic diseases with atrial fibrillation risk: A systematic review and meta-analysis

Emerging wearable technology able to monitor electrocardiogram (ECG) continuously for long periods of time without disrupting the patient’s daily life represents a great opportunity to improve suboptimal current diagnosis of paroxysmal atrial fibrillation (AF). However, its integration into clinical practice is still limited because the acquired ECG recording is often strongly contaminated by transient noise, thus leading to numerous false alarms of AF and requiring manual interpretation of extensive amounts of ECG data. To improve this situation, automated selection of ECG segments with sufficient quality for precise diagnosis has been widely proposed, and numerous algorithms for such ECG quality assessment can be found. Although most have reported successful performance on ECG signals acquired from healthy subjects, only a recent algorithm based on a well-known pre-trained convolutional neural network (CNN), such as AlexNet, has maintained a similar efficiency in the context of paroxysmal AF. Hence, having in mind the latest major advances in the development of neural networks, the main goal of this work was to compare the most recent pre-trained CNN models in terms of classification performance between high- and low-quality ECG excerpts and computational time. In global values, all reported a similar classification performance, which was significantly superior than the one provided by previous methods based on combining hand-crafted ECG features with conventional machine learning classifiers. Nonetheless, shallow networks (such as AlexNet) trended to detect better high-quality ECG excerpts and deep CNN models to identify better noisy ECG intervals. The networks with a moderate depth of about 20 layers presented the best balanced performance on both groups of ECG excerpts. Indeed, GoogLeNet (with a depth of 22 layers) obtained very close values of sensitivity and specificity about 87%. It also maintained a misclassification rate of AF episodes similar to AlexNet and an acceptable computation time, thus constituting the best alternative for quality assessment of wearable, long-term ECG recordings acquired from patients with paroxysmal AF. 

Comparison of Supervised Learning Algorithms for Quality Assessment of Wearable Electrocardiograms with Paroxysmal Atrial Fibrillation

In recent years, the mechanistic interaction between brain and heart has been explored in detail, which explains the effects of brain injuries on the heart and those of cardiac dysfunction on the brain. Brain injuries are the predominant cause of post-stroke deaths, and cardiac dysfunction is the second leading cause of mortality after stroke onset. Several studies have reported the association between brain injuries and cardiac dysfunction. Therefore, it is necessary to study the interaction between the brain and the heart to understand the underlying mechanisms of stroke and cardiac dysfunction. This review focuses on the mechanisms and the effects of cardiac dysfunction after the onset of stroke (ischaemic or haemorrhagic stroke). Moreover, the role of the site of stroke and the underlying mechanisms of the brain-heart axis after stroke onset, including the hypothalamic-pituitary-adrenal axis, inflammatory and immune responses, brain-multi-organ axis, are discussed.

Brain-heart axis and the inflammatory response: connecting stroke and cardiac dysfunction.

Atrial fibrillation and stroke: A review and new insights

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Atrial fibrillation and stroke: A review and new insights