SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these devices based on the 2020 European Society of Cardiology (ESC) guidelines for AF diagnosis in clinical practice. We developed a deep learning model that utilizes RR interval frames for precise, beat-wise classification of electrocardiogram (ECG) signals. This model is specifically designed to sequentially classify each R peak on the ECG, considering the rhythms surrounding each beat. It features a two-stage bidirectional Recurrent Neural Network (RNN) with a many-to-many architecture, which is particularly optimized for processing sequential and time-series data. The structure aims to extract local features and capture long-term dependencies associated with AF. After inference, outputs which indicating either AF or non-AF, derived from various temporal sequences are combined through an ensembling technique to enhance prediction accuracy. We collected AF data from a clinical trial that utilized the MEMO Patch, an adhesive patch-type electrocardiograph. When trained on public databases, the model demonstrated high accuracy on the patch dataset (accuracy: 0.986, precision: 0.981, sensitivity: 0.979, specificity: 0.992, and F1 score: 0.98), maintaining consistent performance across public datasets. SeqAFNet was robust for AF classification, making it a potential tool in real-world applications.

Validity of International Classification of Diseases, Tenth Revision, codes for atrial fibrillation/flutter in critically ill patients with sepsis.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are frequently seen in critically ill sepsis patients and are associated with poor outcomes. There is a need for further research, however, studies are limited due to challenges in identifying patient cohorts. Administrative data using the International Classification of Diseases, Tenth Revision (ICD-10) are routinely used for identifying disease cohorts in large datasets. However, the validity of ICD-10 for AF/AFL remains unexplored in these populations. METHODS: This validation study included 6554 adults with sepsis and septic shock admitted to the intensive care unit. We sought to determine whether ICD-10 coding could accurately identify patients with and without AF/AFL compared to manual chart review. We also evaluated whether the date of ICD-10 code entry could distinguish prevalent from incident AF/AFL, presuming codes dated during the index admission to be incident AF/AFL. A manual chart review was performed on 400 randomly selected patients for confirmation of AF/AFL, and validity was measured using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Among the 400 randomly selected patients, 293 lacked ICD-10 codes for AF/AFL. The manual chart review confirmed the absence of AF/AFL in 286 patients (NPV 97.3%, specificity 99.7%). Among the 107 patients with ICD-10 codes for AF/AFL, 106 were confirmed to have AF/AFL by manual chart review (PPV 99.1%, sensitivity 93.0%). Out of the 114 patients with confirmed AF/AFL, 44 had ICD-10 codes dated during the index admission. All 44 were confirmed to have AF/AFL, however, 18 patients had prior documentation of AF/AFL (incident AF/AFL: PPV 59.1%). Specificity for incident (95.1%) and prevalent (99.7%) AF/AFL were high; however, sensitivity was 76.5% and 77.5%, respectively. DISCUSSION/CONCLUSION: ICD-10 codes perform well in identifying clinical AF/AFL in critically ill sepsis. However, their temporal specificity in distinguishing incidents from prevalent AF/AFL is limited.

Development and Application of an Attribute-Based Taxonomy on the Benefits of Oral Anticoagulant Switching in Atrial Fibrillation: A Delphi Study.

INTRODUCTION: Patients with atrial fibrillation (AF) often switch between oral anticoagulants (OACs). It can be hard to know why a patient has switched outside of a clinical setting. Medication attribute comparisons can suggest benefits. Consensus on terms and definitions is required for inferring OAC switch benefits. The objectives of the study were to generate consensus on a taxonomy of the potential benefits of OAC switching in patients with AF and apply the taxonomy to real-world data. METHODS: Nine expert clinicians (seven clinical pharmacists, two cardiologists) with at least 3 years of clinical and research experience in AF participated in a Delphi process. The experts rated and commented on a proposed taxonomy on the potential benefits of OAC switching. After each Delphi round, ratings were analyzed with the RAND Corporation/University of California, Los Angeles (RAND/UCLA) appropriateness method. Median ratings, disagreement index, and comments were used to modify the taxonomy. The resulting taxonomy from the Delphi process was applied to a cohort of patients with AF who switched OACs in a population-based administrative health dataset from 1996 to 2019 in British Columbia, Canada. RESULTS: The taxonomy was finalized in two Delphi rounds, reaching consensus on five switch benefit categories: safety, effectiveness, convenience, economic considerations, and drug interactions. Safety benefit (a switch that could lower the risk of adverse drug events) had three subcategories: major bleeding, intracranial hemorrhage (ICH), and gastrointestinal (GI) bleeding. Effectiveness benefit had four subcategories: stroke and systemic embolism (SSE), ischemic stroke, myocardial infarction (MI), and all-cause mortality. Real-world OAC switches revealed that more OAC switches had convenience (72.6%) and drug interaction (63.0%) benefits compared to effectiveness (SSE 22.0%, ischemic stroke 11.1%, MI 3.1%, all-cause mortality 10.1%), safety (major bleeding 24.3%, GI bleeding 10.6%, ICH 48.5%), and economic benefits (12.1%). CONCLUSIONS: The Delphi-based taxonomy identified five criteria for the beneficial effects of OAC switching, aiding in characterizing real-world OAC switching.

Pulsed Field or Conventional Thermal Ablation for Paroxysmal Atrial Fibrillation.

BACKGROUND: Catheter-based pulmonary vein isolation is an effective treatment for paroxysmal atrial fibrillation. Pulsed field ablation, which delivers microsecond high-voltage electrical fields, may limit damage to tissues outside the myocardium. The efficacy and safety of pulsed field ablation as compared with conventional thermal ablation are not known. METHODS: In this randomized, single-blind, noninferiority trial, we assigned patients with drug-refractory paroxysmal atrial fibrillation in a 1:1 ratio to undergo pulsed field ablation or conventional radiofrequency or cryoballoon ablation. The primary efficacy end point was freedom from a composite of initial procedural failure, documented atrial tachyarrhythmia after a 3-month blanking period, antiarrhythmic drug use, cardioversion, or repeat ablation. The primary safety end point included acute and chronic device- and procedure-related serious adverse events. RESULTS: A total of 305 patients were assigned to undergo pulsed field ablation, and 302 were assigned to undergo thermal ablation. At 1 year, the primary efficacy end point was met (i.e., no events occurred) in 204 patients (estimated probability, 73.3%) who underwent pulsed field ablation and 194 patients (estimated probability, 71.3%) who underwent thermal ablation (between-group difference, 2.0 percentage points; 95% Bayesian credible interval, -5.2 to 9.2; posterior probability of noninferiority, >0.999). Primary safety end-point events occurred in 6 patients (estimated incidence, 2.1%) who underwent pulsed field ablation and 4 patients (estimated incidence, 1.5%) who underwent thermal ablation (between-group difference, 0.6 percentage points; 95% Bayesian credible interval, -1.5 to 2.8; posterior probability of noninferiority, >0.999). CONCLUSIONS: Among patients with paroxysmal atrial fibrillation receiving a catheter-based therapy, pulsed field ablation was noninferior to conventional thermal ablation with respect to freedom from a composite of initial procedural failure, documented atrial tachyarrhythmia after a 3-month blanking period, antiarrhythmic drug use, cardioversion, or repeat ablation and with respect to device- and procedure-related serious adverse events at 1 year. (Funded by Farapulse-Boston Scientific; ADVENT ClinicalTrials.gov number, NCT04612244.).

Expert-enhanced machine learning for cardiac arrhythmia classification.

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as "excellent" according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

Mendelian randomization study on atrial fibrillation and cardiovascular disease subtypes.

Atrial fibrillation (AF) has been associated with numerous diseases. However, whether AF is a cause or consequence of these diseases is uncertain. To clarify, we assessed the causal role of AF on ischemic heart disease (IHD), stroke, other cardiovascular disease (CVD) subtypes, type 2 diabetes mellitus (T2DM), and late-onset AD using bi-directional two-sample Mendelian randomization (MR) among people primarily of European descent. Genetically predicted log odds of AF was associated with any stroke (odds ratio (OR) 1.22, 95% CI 1.18 to 1.27), particularly cardioembolic stroke and possibly subdural hemorrhage, with sensitivity analyses showing similar positive findings. Genetically predicted AF was also associated with arterial thromboembolism (1.32, 1.13 to 1.53), and heart failure (1.26, 1.21 to 1.30). No association of genetically predicted AF with IHD, T2DM, cognitive function, or late-onset AD was found. Conversely, genetically predicted IHD, heart failure and possibly ischemic stroke, particularly cardioembolic stroke, were positively associated with AF. Atrial fibrillation plays a role in any stroke, arterial thromboembolism, and heart failure, corroborating current clinical guidelines on the importance of preventing these complications by effective AF management. In addition, patients with IHD, heart failure or possibly ischemic stroke might be predisposed to developing AF, with implications for management.

Formula to estimate left atrial volume using antero-posterior diameter in patients with catheter ablation of atrial fibrillation.

ABSTRACT: In patients undergoing atrial fibrillation (AF) ablation, an enlarged left atrium (LA) is a predictor of procedural failure as well as AF recurrence on long term. The most used method to assess LA size is echocardiography-measured diameter, but the most accurate remains computed tomography (CT).The aim of our study was to determine whether there is an association between left atrial diameters measured in echocardiography and the left atrial volume determined by CT in patients who underwent AF ablation.The study included 93 patients, of whom 60 (64.5%) were men and 64 (68.8%) had paroxysmal AF, who underwent AF catheter ablation between January 2018 and June 2019. Left atrial diameters in echocardiography were measured from the long axis parasternal view and the LA volume in CT was measured on reconstructed three-dimensional images.The LA in echocardiography had an antero-posterior (AP) diameter of 45.0 ±â€Š6 mm (median 45; Inter Quartile Range [IQR] 41-49, range 25-73 mm), longitudinal diameter of 67.5 ±â€Š9.4 (median 66; IQR 56-88, range 52-100 mm), and transversal diameter of 42 ±â€Š8.9 mm (IQR 30-59, range 23-64.5 mm). The volume in CT was 123 ±â€Š29.4 mL (median 118; IQR 103-160; range 86-194 mL). We found a significant correlation (r = 0.702; P < .05) between the AP diameter and the LA volume. The formula according to which the AP diameter of the LA can predict the volume was: LA volume = AP diam3 + 45 mL.There is a clear association between the left atrial AP diameter measured on echocardiography and the volume measured on CT. The AP diameter might be sufficient to determine the increase in the volume of the atrium and predict cardiovascular outcomes.

Differences in Healthcare Use Between Patients With Persistent and Paroxysmal Atrial Fibrillation Undergoing Catheter-Based Atrial Fibrillation Ablation: A Population-Based Cohort Study From Ontario, Canada.

Background Patients with persistent atrial fibrillation (AF) undergoing catheter-based AF ablation have lower success rates than those with paroxysmal AF. We compared healthcare use and clinical outcomes between patients according to their AF subtypes. Methods and Results Consecutive patients undergoing AF ablation were prospectively identified from a population-based registry in Ontario, Canada. Via linkage with administrative databases, we performed a retrospective analysis comparing the following outcomes between patients with persistent and paroxysmal AF: healthcare use (defined as AF-related hospitalizations/emergency room visits), periprocedural complications, and mortality. Multivariable Poisson modeling was performed to compare the rates of AF-related and all-cause hospitalizations/emergency room visits in the year before versus after ablation. Between April 2012 and March 2016, there were 3768 consecutive patients who underwent first-time AF ablation, of whom 1040 (27.6%) had persistent AF. The mean follow-up was 1329 days. Patients with persistent AF had higher risk of AF-related hospitalization/emergency room visits (hazard ratio [HR], 1.21; 95% CI, 1.09-1.34), mortality (HR, 1.74; 95% CI, 1.15-2.63), and periprocedural complications (odds ratio, 1.36; 95% CI, 1.02-1.75) than those with paroxysmal AF. In the overall cohort, there was a 48% reduction in the rate of AF-related hospitalization/emergency room visits in the year after versus before ablation (rate ratio [RR], 0.52; 95% CI, 0.48-0.56). This reduction was observed for patients with paroxysmal (RR, 0.45; 95% CI, 0.41-0.50) and persistent (RR, 0.74; 95% CI, 0.63-0.87) AF. Conclusions Although patients with persistent AF had higher risk of adverse outcomes than those with paroxysmal AF, ablation was associated with a favorable reduction in downstream AF-related healthcare use, irrespective of AF type.

Baroreflex Sensitivity in Patients With Atrial Fibrillation.

Background It has been reported that atrial fibrillation (AF) may contribute to impairment of baroreflex sensitivity (BRS). However, the difference of BRS between patients with persistent AF (PeAF) and those with paroxysmal AF (PAF) is unknown. We tested the hypothesis that patients with PeAF have a more impaired BRS compared with those with PAF. Methods and Results From October 2015 onwards, a total of 67 patients (14 women [20.9%]; mean age 65.2±10.1 years) with PAF (n=46, 68.7%) and PeAF (n=21, 31.3%), who underwent catheter ablation, were prospectively enrolled. The baseline BRS was evaluated during sinus rhythm. The baseline BRS in patients with PeAF was significantly lower than those with PAF (2.97 [0.52-6.62] ms/mm Hg versus 4.70 [2.36-8.37] ms/mm Hg, P=0.047). The BRS was significantly depressed after catheter ablation in all the patients (4.66 [1.80-7.37] ms/mm Hg versus 0.55 [-0.15 to 1.22] ms/mm Hg, P<0.001). However, the depression of BRS because of catheter ablation appeared attenuated in patients with PeAF when compared with those with PAF. The number of patients who did not show depression of BRS was significantly greater, that is, patients with PeAF (3/12, 25%) than those with PAF (0/46, 0%, P<0.01). Conclusions Our findings demonstrated that the baseline BRS was more depressed in patients with PeAF compared with PAF. Catheter ablation depressed BRS irrespective of the type of AF, with a greater effect in patients with PAF than PeAF.

The 2020 Canadian Cardiovascular Society/Canadian Heart Rhythm Society Comprehensive Guidelines for the Management of Atrial Fibrillation.

The Canadian Cardiovascular Society (CCS) atrial fibrillation (AF) guidelines program was developed to aid clinicians in the management of these complex patients, as well as to provide direction to policy makers and health care systems regarding related issues. The most recent comprehensive CCS AF guidelines update was published in 2010. Since then, periodic updates were published dealing with rapidly changing areas. However, since 2010 a large number of developments had accumulated in a wide range of areas, motivating the committee to complete a thorough guideline review. The 2020 iteration of the CCS AF guidelines represents a comprehensive renewal that integrates, updates, and replaces the past decade of guidelines, recommendations, and practical tips. It is intended to be used by practicing clinicians across all disciplines who care for patients with AF. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system was used to evaluate recommendation strength and the quality of evidence. Areas of focus include: AF classification and definitions, epidemiology, pathophysiology, clinical evaluation, screening and opportunistic AF detection, detection and management of modifiable risk factors, integrated approach to AF management, stroke prevention, arrhythmia management, sex differences, and AF in special populations. Extensive use is made of tables and figures to synthesize important material and present key concepts. This document should be an important aid for knowledge translation and a tool to help improve clinical management of this important and challenging arrhythmia.

Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.

BACKGROUND: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. METHODS AND RESULTS: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P<0.001). CONCLUSIONS: We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.

Subtype of atrial fibrillation and the outcome of transcatheter aortic valve replacement: The FinnValve Study.

Whether the subtype of atrial fibrillation affects outcomes after transcatheter aortic valve replacement for aortic stenosis is unclear. The nationwide FinnValve registry included 2130 patients who underwent primary after transcatheter aortic valve replacement for aortic stenosis during 2008-2017. Altogether, 281 (13.2%) patients had pre-existing paroxysmal atrial fibrillation, 651 (30.6%) had pre-existing non-paroxysmal atrial fibrillation and 160 (7.5%) were diagnosed with new-onset atrial fibrillation during the index hospitalization. The median follow-up was 2.4 (interquartile range: 1.6-3.8) years. Paroxysmal atrial fibrillation did not affect 30-day or overall mortality (p-values >0.05). Non-paroxysmal atrial fibrillation demonstrated an increased risk of overall mortality (hazard ratio: 1.61, 95% confidence interval: 1.35-1.92; p<0.001), but not 30-day mortality (p = 0.084). New-onset atrial fibrillation demonstrated significantly increased 30-day mortality (hazard ratio: 2.76, 95% confidence interval: 1.25-6.09; p = 0.010) and overall mortality (hazard ratio: 1.68, 95% confidence interval: 1.29-2.19; p<0.001). The incidence of early or late stroke did not differ between atrial fibrillation subtypes (p-values >0.05). In conclusion, non-paroxysmal atrial fibrillation and new-onset atrial fibrillation are associated with increased mortality after transcatheter aortic valve replacement for aortic stenosis, whereas paroxysmal atrial fibrillation has no effect on mortality. These findings suggest that non-paroxysmal atrial fibrillation rather than paroxysmal atrial fibrillation may be associated with structural cardiac damage which is of prognostic significance in patients with aortic stenosis undergoing transcatheter aortic valve replacement.

Genetics and Epigenetics of Atrial Fibrillation.

Atrial fibrillation (AF) is known to be the most common supraventricular arrhythmia affecting up to 1% of the general population. Its prevalence exponentially increases with age and could reach up to 8% in the elderly population. The management of AF is a complex issue that is addressed by extensive ongoing basic and clinical research. AF centers around different types of disturbances, including ion channel dysfunction, Ca2+-handling abnormalities, and structural remodeling. Genome-wide association studies (GWAS) have uncovered over 100 genetic loci associated with AF. Most of these loci point to ion channels, distinct cardiac-enriched transcription factors, as well as to other regulatory genes. Recently, the discovery of post-transcriptional regulatory mechanisms, involving non-coding RNAs (especially microRNAs), DNA methylation, and histone modification, has allowed to decipher how a normal heart develops and which modifications are involved in reshaping the processes leading to arrhythmias. This review aims to provide a current state of the field regarding the identification and functional characterization of AF-related epigenetic regulatory networks.

Prevalence of Atrial Fibrillation Subtypes in Italy and Projections to 2060 for Italy and Europe.

BACKGROUND/OBJECTIVES: Atrial fibrillation (AF) subtypes may carry different cardiovascular risk profiles, but information on their frequency from population-based studies is lacking. We estimated prevalence of AF subtypes in a representative sample of the Italian older population, projecting figures for Italy and the European Union. DESIGN: Cross-sectional study. SETTING: Three primary care practices in northern, central, and southern Italy. PARTICIPANTS: All individuals aged 65 years or older, for a total sample of 6,016 subjects. Excluding 235 noneligible, participation was 78.3%, which left 4,528 participants. MEASUREMENTS: A double systematic and opportunistic screening procedure identified possible AF cases, followed by clinical and electrocardiogram confirmation. Patients were categorized with paroxysmal, persistent, or permanent AF. Prevalence was calculated by sex and 5-year age groups. Prevalence figures were applied to population projections for all 28 European Union states to estimate AF subtypes expected in future decades. RESULTS: In the 4,528 participants (mean age = 74.5 ± 6.8 years; 47.2% men), 331 AF cases were identified: 140 (42.3%) paroxysmal, 77 (23.3%) persistent, and 114 (34.4%) permanent. Prevalence was 3.1% (95% confidence interval (CI) = 2.6%-3.6%) for paroxysmal, 1.7% (95% CI = 1.4%-2.1%) for persistent, and 2.5% (95% CI = 2.1%-3.0%) for permanent AF. Italian older persons having AF in 2016 were estimated at approximately 449,000 for paroxysmal, approximately 240,000 for persistent, and approximately 391,000 for permanent AF, projected to increase in 2060 to approximately 785,000, approximately 358,000, and approximately 748,000, respectively. European Union older persons having AF in 2016 were estimated at approximately 3,185,000 for paroxysmal, approximately 1,722,000 for persistent, and approximately 2,710,000 for permanent AF, projected to increase in 2060 to approximately 5,989,000, approximately 2,833,000, and approximately 5,579,000, respectively. CONCLUSION: We provided first projections of AF subtypes for Italy and Europe. The worse cardiovascular risk profile of persistent and permanent forms indicates an increased burden in future decades.

A novel framework for noninvasive analysis of short-term atrial activity dynamics during persistent atrial fibrillation.

ECG-based representation of atrial fibrillation (AF) progression is currently limited. We propose a novel framework for a more sensitive noninvasive characterization of the AF substrate during persistent AF. An atrial activity (AA) recurrence signal is computed from body surface potential map (BSPM) recordings, and a set of characteristic indices is derived from it which captures the short- and long-term recurrent behaviour in the AA patterns. A novel measure of short- and long-term spatial variability of AA propagation is introduced, to provide an interpretation of the above indices, and to test the hypothesis that the variability in the oscillatory content of AA is due mainly to a spatially uncoordinated propagation of the AF waveforms. A simple model of atrial signal dynamics is proposed to confirm this hypothesis, and to investigate a possible influence of the AF substrate on the short-term recurrent behaviour of AA propagation. Results confirm the hypothesis, with the model also revealing the above influence. Once the characteristic indices are normalized to remove this influence, they show to be significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. Therefore, the proposed framework improves noninvasive AF substrate characterization in patients with a very similar substrate. Graphical Abstract Schematic representation of the proposed framework for the noninvasive characterization of short-term atrial signal dynamics during persistent AF. The proposed framework shows that the faster the AA is propagating, the more stable its propagation paths are in the short-term (larger values of Speed in the bottom right plot should be interpreted as lower speed of propagation of the corresponding AA propagation patters).

Different Types of Atrial Fibrillation Share Patterns of Gut Microbiota Dysbiosis.

Dysbiotic gut microbiota (GM) and disordered metabolic patterns are known to be involved in the clinical expression of atrial fibrillation (AF). However, little evidence has been reported in characterizing the specific changes in fecal microbiota in paroxysmal AF (PAF) and persistent AF (psAF). To provide a comprehensive understanding of GM dysbiosis in AF types, we assessed the GM signatures of 30 PAF patients, 20 psAF patients, and 50 non-AF controls based on metagenomic and metabolomic analyses. Compared with control subjects, similar changes of GM were identified in PAF and psAF patients, with elevated microbial diversity and similar alteration in the microbiota composition. PAF and psAF patients shared the majority of differential taxa compared with non-AF controls. Moreover, the similarity was also illuminated in microbial function and associated metabolic alterations. Additionally, minor disparity was observed in PAF compared with psAF. Several distinctive taxa between PAF and psAF were correlated with certain metabolites and atrial diameter, which might play a role in the pathogenesis of atrial remodeling. Our findings characterized the presence of many common features in GM shared by PAF and psAF, which occurred at the self-terminating PAF. Preventative and therapeutic measures targeting GM for early intervention to postpone the progression of AF are highly warranted.IMPORTANCE Atrial fibrillation has been identified to be associated with disordered gut microbiota. Notably, atrial fibrillation is a progressive disease and could be categorized as paroxysmal and persistent based on the duration of the episodes. The persistent atrial fibrillation patients are accompanied by higher risk of stroke and lower success rate of rhythm control. However, the microbial signatures of different categories of atrial fibrillation patients remain unknown. We sought to determine whether disordered gut microbiota occurs in the self-terminating PAF or intestinal flora develops dynamically during atrial fibrillation progression. We found that different types of atrial fibrillation show a limited degree of gut microbiota shift. Gut microbiota dysbiosis has already occurred in mild stages of atrial fibrillation, which might act as an early modulator of disease, and therefore may be regarded as a potential target to postpone atrial fibrillation progression.

Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time-frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

Effect of acute and chronic ethanol on atrial fibrillation vulnerability in rats.

BACKGROUND: Even though ethanol consumption has been associated with risk of atrial fibrillation (AF), little is known about how ethanol affects atrial electrophysiology. OBJECTIVE: The purpose of this study was to study the electrophysiological effect of ethanol on rat AF. METHODS: Atrial optical mapping was performed on male Long Evans rat hearts with escalating concentrations of ethanol (0, 1, 2, and 3 mM). In addition, patch-clamp recordings on isolated atrial myocytes were performed. In chronic ethanol study, rats were divided into control and chronic ethanol groups (20% ethanol in drinking water for 6 months). Atrial optical mapping, histology, immunohistochemistry, and reverse transcriptase polymerase chain reaction were performed in chronic rats. RESULTS: Acute ethanol perfusion increased AF vulnerability (0% at 0 mM, 0% at 1 mM, 57.1% at 2 mM, and 100% at 3 mM) in a dose-related response. Ethanol infusion decreased conduction velocities (CVs) in both atria and shortened effective refractory periods (ERP) only in the right atria with increased in dispersion of refractoriness. Action potential duration at 50% and 90% repolarization from right atrial myocytes were shortened, with corresponding increase of sustained potassium current. Chronic ethanol consumption increased AF inducibility (10% control vs 95.2% chronic ethanol). CVs in both atria were significantly decreased. ERP of the right atrium was shortened, and dispersion of ERP was increased. Expression (mRNA) of KCNQ1 and connexin40 were increased, but KCNA5 was decreased in the right atrium of rats exposed to chronic ethanol. CONCLUSION: Acute and chronic exposure to ethanol increases AF vulnerability by slowing CV, shortening right atrial ERP, and increasing dispersion of ERP.

The left atrial appendage morphology is associated with embolic stroke subtypes using a simple classification system: A proof of concept study.

BACKGROUND AND PURPOSE: The current left atrial appendage (LAA) classification system (cLAA-CS) categorizes it into 4 morphologies: chicken wing (CW), windsock, cactus, and cauliflower, though there is limited data on either reliability or associations between different morphologies and stroke risk. We aimed to develop a simplified LAA classification system and to determine its relationship to embolic stroke subtypes. METHODS: Consecutive patients with ischemic stroke from a prospective stroke registry who previously underwent a clinically-indicated chest CT were included. Stroke subtype was determined and LAA morphology was classified using the traditional system (in which CW = low risk) and a new system (LAA-H/L, in which low risk morphology (LAA-L) was defined as an acute angle bend or fold from the proximal/middle portion of the LAA and high risk morphology (LAA-H) was defined as all others). As a proof of concept study, we determined reliability for the two classification systems, and we assessed the associations between both classification systems with stroke subtypes in our cohort and previous studies. RESULTS: We identified 329 ischemic stroke patients with a qualifying chest CT (126 cardioembolic subtype, 116 embolic stroke of undetermined source (ESUS), and 87 non-cardioembolic subtypes). Intra- and inter-rater agreements improved using the LAA-H/L (0.95 and 0.85, respectively) vs. cLAA-CS (0.50 and 0.40). The LAA-H/L led to classifying 69 LAA morphologies that met criteria for CW as LAA-H. In fully adjusted models, LAA-H was associated with cardioembolic stroke (OR 5.4, 95%CI 2.1-13.7) and ESUS (OR 2.8 95% CI 1.2-6.4). Non-CW morphology was also associated with embolic stroke subtypes, but the effect size was much less pronounced. Studies using the cLAA-CS yielded mixed results for inter- and intra-rater agreements but most showed an association between a non-CW morphology and stroke with no difference among the three non-CW subtypes. CONCLUSION: The LAA-H/L classification system is simple, has excellent intra and inter-rater agreements, and may help risk identify patients with cardioembolic stroke subtypes. Larger studies are needed to validate these findings.

Atrial fibrillation classification based on convolutional neural networks.

BACKGROUND: The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990-2010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital. METHODS: Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2). RESULTS: In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5,268,818 parameters and the training time of 89 s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253 s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased. CONCLUSION: For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.