Atrial Fibrillation Electrical Remodelling via Ablation of the Epicardial Neural Networks and Suprathreshold Stimulation of Vagosympathetic Nerve

The present study aimed to assess the effect of sigma-1 receptor (S1R) stimulation on autonomic nerve dysfunction and susceptibility to atrial fibrillation (AF) in a rat depression model. Male rats were randomly divided into one of the following four treatment groups: saline [control (CTL)]; saline + intragastric administration of SA4503, an agonist of S1R (CTS); chronic unpredictable mild stress (CUMS) to produce depression (MDD); and CUMS + intragastric administration of SA4503 (MDS). Depression-like behaviors, such as reduced sucrose preference, decreased body weight gain, and increased immobility time during forced swimming, improved in the MDS group after 4 wk of SA4503 treatment. Compared with rats in the CTL group, rats in the MDD group showed significantly augmented sympathetic activity, reduced parasympathetic activity, decreased heart rate variability, and lowered S1R expression in the atrium and hippocampus (all P < 0.01). However, rats in the MDS group showed mitigated aforementioned alterations and improved electrical remodeling compared with rats in the MDD group (all P < 0.01). Furthermore, rats in the MDS group showed shortened activation latencies, increased effective refractory periods, and lowered frequency of AF incidence duration and fibrosis compared with rats in the MDD group (all P < 0.01). The results indicate that S1R stimulation reduces sympathetic activity and susceptibility to AF by improving depressive behaviors, modulating cardiac autonomic nerve balance, lightening nerve remodeling, and upregulating S1R and ion channel protein expression. NEW & NOTEWORTHY Chronic stimulation of the sigma-1 receptor (S1R) ameliorates depression-induced autonomic nerve dysfunction by modulating the imbalance between overactivated sympathetic activity and decreased vagal activity. Chronic S1R stimulation alleviates atrial electrical remodeling, fibrosis, and susceptibility to atrial fibrillation (AF). The S1R agonist may target the underlying mechanisms related to AF occurrence. The results indicate that the S1R could be a potential clinical target for atrial arrhythmia, especially when it is combined with major depressive disorders.

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Response by Andreas et al to Letter Regarding Article, “Electrical Stimulation of the Greater Auricular Nerve to Reduce Postoperative Atrial Fibrillation”

Circulation Arrhythmia and Electrophysiology ◽

10.1161/circep.119.008067 ◽

2019 ◽

Vol 12 (12) ◽

Author(s):

Martin Andreas ◽

Philipp Arzl ◽

Andreas Mitterbauer ◽

Nicolas M. Ballarini ◽

Frieda-Maria Kainz ◽

...

Keyword(s):

Atrial Fibrillation ◽

Electrical Stimulation ◽

Postoperative Atrial Fibrillation ◽

Stimulation Of

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Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry

Rational Pharmacotherapy in Cardiology ◽

10.20996/1819-6446-2019-15-3-379-385 ◽

2019 ◽

Vol 15 (3) ◽

pp. 379-385

Author(s):

Z. G. Tatarintseva ◽

E. D. Kosmacheva ◽

S. V. Kruchinova ◽

V. A. Akinshina ◽

A. A. Khalafyan

Keyword(s):

Atrial Fibrillation ◽

Neural Networks ◽

Acute Coronary Syndrome ◽

Predictive Models ◽

Fatal Outcome ◽

Prognostic Models ◽

Lethal Outcome ◽

Clinical Indicators ◽

Coronary Syndrome ◽

Clinical Indices

With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.

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