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Objectives: To determine the clinical course of fetal isolated non-immune-mediated second-degree atrioventricular block (AVB) and the factors associated with spontaneous recovery in these cases., Methods: Fetuses with isolated non-immune-mediated second-degree AVB were recruited prospectively between 2014 and 2022. These fetuses were divided into two groups: those which recovered spontaneously and those which did not. Maternal and fetal characteristics and intrauterine and postnatal outcomes were compared between the two groups., Results: The study cohort included 20 fetuses with isolated non-immune-mediated second-degree AVB, diagnosed at a median gestational age of 22.0 (range, 17.0-35.0) weeks. In 12 fetuses, 1:1 atrioventricular conduction was restored spontaneously in utero and there was no recurrence during the postnatal follow-up period. In the remaining eight fetuses, second-degree AVB was maintained and, in six of these, the pregnancy was terminated on parental request. Of the two liveborn children who had persistent second-degree AVB prenatally, one had progressed to complete AVB at the latest follow-up, at the age of 34 months, but was asymptomatic, without heart enlargement or dysfunction. The other child progressed to complete AVB after delivery and was diagnosed with type-2 long QT syndrome. This infant died aged 2 months. Fetuses in the group that recovered spontaneously had earlier gestational age at diagnosis (median, 20.0 (range, 17.0-26.0) vs 24.5 (range, 18.0-35.0) weeks; P = 0.004) and higher atrial rate at diagnosis (median, 147 (range, 130-160) vs 138 (range, 125-149) bpm; P = 0.006) in comparison with the group that did not recover spontaneously. The best cut-off values for prediction of failure to recover spontaneously were 22.5 weeks' gestational age at diagnosis and 144 bpm atrial rate at diagnosis, with sensitivities of 87.5% and 75.0%, respectively, and specificities of 92.0% and 87.5%, respectively., Conclusions: The outcome of 60% of fetuses with isolated non-immune-mediated second-degree AVB was favorable. Earlier gestational age and higher atrial rate at diagnosis were associated with spontaneous reversion to normal sinus rhythm. Prenatal genetic testing should be performed in cases with persistent AVB, to exclude heritable disorders including long QT syndrome. These findings provide important information for clinical management and prenatal counseling in these cases. © 2024 International Society of Ultrasound in Obstetrics and Gynecology., (© 2024 International Society of Ultrasound in Obstetrics and Gynecology.)

Introduction: Annual healthcare expenditures associated with atrial fibrillation (AF) in the United States (US) continue to grow as more symptomatic patients present to emergency departments (ED). Predictors of spontaneous conversion to normal sinus rhythm (ScNSR) remain poorly understood, as well as the timeline of ScNSR remains unclear. We sought to 1) to assess the association of key demographics, anthropometric, and clinical factors to ScNSR and 2) to evaluate the timeline of ScNSR, and 3) determine clinical predictors of ScNSR., Methods: This single center, retrospective cohort study analyzed patients aged ≥18 years with symptomatic AF as diagnosed and evaluated through the ED of a rural tertiary care center in West Virginia from September 2015 to December 2018., Results: Our cohort consisted of 375 AF patients (mean age 65 years, 54% male). A total of 177 patients attained ScNSR either in the ED or after hospital admission with a mean conversion time of 14.7 h (±12). Onset of symptoms <24 hrs has strong positive association to ScNSR 3.97 (95% CI: 2.24-7.05; p < 0.0001). Male gender 0.55 (95% CI: 0.35-0.85; p = 0.007) and hypertension 0.48 (95% CI: 0.31-0.76; p = 0.002), showed a strong negative association to ScNSR. Of the patients that converted spontaneously (177), the majority, 136 (76.8%) achieved ScNSR within 24 h of ED triage without use of electrical or chemical cardioversion., Conclusion: Most patients with AF in the ED converted spontaneously to sinus rhythm within the first 24 h which underscores the importance of earlier watchful waiting over interventions to achieve normal sinus rhythm (NSR)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Indian Heart Rhythm Society. Published by Elsevier B.V. All rights reserved.)

Background and Aims: It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks., Methods: This retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) - 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis., Results: The prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83., Conclusion: The AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Kim, Joo, Jeon, Kim, Shin, Im and Park.)

Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN -1-layer Transformer hybrid R model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87) R ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kudo, Chen, Zhou, Izu, Chen-Izu, Zhu, Tamura, Kanaya and Huang.)

Background: The most-commonly used multi-slice Simpson's method employed with routine two-dimensional segmented cine images makes it difficult to evaluate left ventricular (LV) volume and function due to endocardial border blurring and beat-to-beat variation during atrial fibrillation (AF) status., Objectives: To assess the feasibility of compressed sensing real-time (CSRT) cine imaging combined with an area-length method for quantification of LV systolic function in normal sinus rhythm (NSR) and AF., Methods: The CSRT cine sequence and routine segmented balanced Steady-State-Free-Precession cine sequence were performed in 71 patients with NSR ( n = 36) or AF ( n = 35). Image quality and edge sharpness for both sequences were assessed. The LV functional measurements in patients with NSR included end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), cardiac output (CO), cardiac index (CI), and LV mass (LVM); all were assessed using segmented cine with Simpson's rule in short axis (SegSA&#95;Simpson, as a reference standard) and area-length (AL) method in the two chamber (Seg2CH&#95;AL) or four chamber (Seg4CH&#95;AL) and CSRT cine with AL method in the two chamber (CSRT2CH&#95;AL) or four chamber (CSRT4CH&#95;AL). Finally, the mean, maximum, and minimum values of each LV functional parameter [EDV/ESV/SV/EF/CO/CI/LVM/heart rate (HR)] from 4~5 consecutive heartbeats were measured using CSRT2CH&#95;AL in patients with AF., Results: In patients with NSR, measurements of EDV ( p > 0.05), ESV ( p > 0.05), SV ( p > 0.05), EF ( p > 0.05), and LVM ( p > 0.05) assessed with CSRT2CH&#95;AL did not differ significantly from those obtained with SegSA&#95;Simpson. In patients with AF, CSRT image quality score ( p < 0.001) and edge sharpness ( p < 0.001) both were significantly higher than those obtained from segmented cine. The CSRT2CH&#95;AL provided significantly different results among mean, maximum, and minimum values of each LV parameter from 4~5 consecutive heartbeats (all p < 0.001) with strong inter- and intra-observer agreement in AF., Conclusions: The CSRT cine sequence combined with two chamber area-length analysis accurately assessed LV systolic function in NSR. This approach is expected to permit the assessment of multiple parameters in consecutive heartbeats with good inter- and intra-observer reproducibility for beat-to-beat analysis of LV function in AF., Competing Interests: JA was employed by Siemens Shenzhen Magnetic Resonance Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yin, Cui, An, Zhao, Yang, Li, Yang, Wang, Dong, Yu, He, Chen, Lu and Zhao.)

Background: Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes., Objective: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machine learning classification., Methods: We obtained ECG signals from 139 active equine athletes (120 control, 19 with a PAF diagnosis). The SPAR method was applied to 9 short (20-second) ECG strips for each subject. An optimal SPAR feature set was determined by forward feature selection for input to a machine learning model ensemble of 3 different classifiers ( k -nearest neighbors, linear support vector machine, and radial basis function kernel support vector machine). Imbalanced data were handled by upsampling the minority (PAF) class. A final subject classification was made by taking a majority vote over results from the 9 ECG strips., Results: Our final cross-validated classification for a subject gave an accuracy of 89.0%, sensitivity of 94.8%, specificity of 87.1%, and receiver operating characteristic area under the curve of 0.98, taking PAF as the positive class., Conclusion: The SPAR method and machine learning generated a final model with high sensitivity, suggesting that PAF can be discriminated from short equine ECG strips. This preliminary study indicated that SPAR analysis of human ECG could support patient monitoring, risk stratification, and clinical decision-making., (© 2022 Heart Rhythm Society.)

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

The cellular action potential of cardiac muscles generates Electrocardiogram (ECG) signals. Disturbances in cardiac cells are determined by analyzing the stability of ECG intervals. The PTa Interval (PTaI) of ECG represents the atrial Action Potential Duration (APD) and the evaluation of the causes of PTaI instability can predict the onset of arrhythmia. This study developed an Autoregressive Moving Average with Exogenous Input (ARMAX) model to explore the roles of Heart Rate Variability (HRV) and Premature Activation (PA) in PTaI dynamics using PTaI and PP Interval (PPI) as exogenous inputs. Minute ECG signals were collected from twenty Normal Sinus Rhythm (NSR) and ten Atrial Tachycardia (AT) volunteers. The EDAN PC ECG system was used in the Modified Limb Lead (MLL) configuration to evaluate instability. The instabilities of PTaI were found at the minimum model orders (A min ) of 10 and 11, in the NSR and AT groups, respectively. In the NSR group, the predominant reason for PTaI instability was HRV, whereas among AT patients, it was largely due to PA that preceded the onset of AT. The proposed model showed better prediction of PTaI with minimum Mean Square Error (MSE) between the measured and predicted PTa Intervals (PTaIs). The factor that led to PTaI instability in AT patients was found to be different from that of the NSR group. The frequency of PA (f PA ) was found to contribute more in the AT than the NSR group. The developed ARMAX model was better in predicting instability of atrial ECG dynamics in both groups than other autoregressive models currently in use.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Background: We sought to investigate and compare the safety and efficacy of two commonly used antiarrhythmic drugs, Dofetilide (DF) and Sotalol (SL), during inpatient drug initiation in patients with symptomatic atrial fibrillation (AF)., Methods: We performed a single center retrospective study of consecutive patients, admitted for initiation of either DF or SL, for AF between 2012 and 2015. Rates of successful cardioversion, QT interval prolongation, adverse events and drug discontinuations were calculated and compared. A two-tailed p value less than 0.05 was considered statistically significant., Results: Of 378 patients, 298 (78.8%) received DF and 80 (21.2%) SL, mean age was 64 ± 11 years, 90% were Caucasians and 66% were males. Among the patients who remained in AF upon admission (DF: 215/298 (72%) vs. SL: 48/80 (60%)), no significant differences were noted in pharmacological cardioversion rates (DF: 125/215(58%) vs. SL: 30/48 (62.5%); p = 0.58). Baseline QTc was similar between the groups, with higher dose dependent QTc prolongation with DF (472.25± 31.3 vs. 458± 27.03; p = 0.008). There were no significant differences in the rates of adverse events such as bradycardia (7.4% vs. 11.3%; p = 0.26), Torsades de pointes (1.3% vs. 1.2%; p = 1.00), and drug discontinuation (9.0% vs. 5.0%; p = 0.47) between the two groups.

The prognostic significance of atrial fibrillation (AF) on mortality in ST-segment elevation myocardial infarction (STEMI) patients is not clearly understood. To elucidate the clinical significance of AF on mortality for 1 year in STEMI patients, we retrospectively analyzed the Korea Acute Myocardial Infarction Registry (KAMIR) database, which spans January 2008 to September 2010 and includes 14,329 patients with acute myocardial infarction. We selected 5,556 patients with marked ECG rhythm (NSR, normal sinus rhythm or AF) on emergency room arrival, < 12 hours of symptom onset, and who underwent primary percutaneous coronary intervention (PCI) within 90 minutes of arriving at the hospital. Patients who had been followed-up for at least for 1 year were analyzed (2,636 of NSR, 119 of AF). At enrollment, AF patients were older (70.7 versus 65.5 years, P < 0.001) and had lower systolic blood pressure (120.6 versus 125.9 mmHg, P = 0.050), a higher heart rate (80.4 versus 75.6/minute, P = 0.009), and a higher rate of Killip III, IV (25.0 versus 14.2%, P = 0.002). Patients with AF showed clearly higher all-cause mortality (22.7 versus 9.5%, HR 2.51, 95%CI 1.68~3.76, P < 0.001) and cardiac death rate (17.7 versus 7.5%, HR 2.49, 95%CI 1.59~3.90, P < 0.001) at 1 year after admission compared patients with NSR. AF induced significantly higher all-cause mortality and cardiac mortality rate in STEMI patients who were appropriately revascularized with primary PCI compared to NSR at 1 year.

Junctional ectopic tachycardia(JET) is a rare childhood arrhythmia originating from the area adjacent to the atrioventricular(AV) node. It often occurs after surgical procedures like repair of Tetralogy of Fallot, atrioventricular septal defect and ventricular septal defect, which are all performed in that area. While AV block (AVB) can occur after JET, it is very rare for late JET occurring after early postoperative AVB to be followed by normal sinus rhythm (NSR). There is no information in the literature related to the pathophysiology of this phenomenon. In this text, we present 4 patients who developed complete AV block(CAVB) in the early postoperative period (within the first 24h) after JET in late period (>72h) and returned to NSR with first-degree AV block and then NSR during follow-up. Based on these cases, we hypothesize that there is a link between late JET after early postoperative CAVB and return to NSR., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objectives: Atrial fibrillation (AF) is associated with less favorable outcomes in patients undergoing mitral valve and tricuspid valve surgery. Despite growing evidence on the potential benefits of surgical ablation for AF there is significant variability among surgeons in treatment of AF. The purpose of our study was to assess the effect of the Cox-maze procedure on operative and follow-up outcomes., Methods: In our prospective study, patients who underwent isolated mitral valve or mitral valve+tricuspid valve surgery without history of AF (n = 506), with untreated AF (n = 75), or with Cox-maze procedure (n = 236) were included (N = 817). Sinus rhythm was captured according to Heart Rhythm Society guidelines. Patients who underwent the Cox-maze procedure were propensity score matched to patients without history of AF resulting in 208 pairs of patients., Results: Operative outcomes were comparable after propensity score matching (Cox-maze procedure vs no AF) stroke/transient ischemic attack (0.5% vs 0.5%; P = 1.00), renal failure (2.9% vs 1.4%; P = .34), and operative mortality (1.4% vs 1.4%; P = 1.00). High return to sinus rhythm was documented at 6, 12, and 24 months (92%, 91%, and 86%, respectively) as well as sinus rhythm off antiarrhythmic drugs (79%, 84%, and 82%, respectively). Incidence of embolic stroke in patients who underwent Cox-maze procedure was 1.7% (4 out of 232 patients) and 5.1 cases per 1000 person-years. No difference in 4-year cumulative survival between propensity score-matched groups (91.9% vs 86.9%; log rank, 1.67; P = .20), but higher for patients who underwent Cox-maze procedure versus patients with untreated AF (hazard ratio, 2.47; P = .048). Higher additive European System for Cardiac Operative Risk Evaluation (odds ratio, 1.40; P < .001) and limited surgeon experience with Cox-maze procedure (odds ratio, 3.60; P < .001) were significant predictors for failure to perform Cox-maze procedure., Conclusions: In our center, 76% of patients undergoing mitral valve or mitral valve+tricuspid valve surgery experiencing AF underwent concomitant Cox-maze procedure, which is considerably higher than the national average. No increased morbidity was associated with the Cox-maze procedure with the benefit of very low thromboembolic rate. These results suggest the need for performance-based education for AF surgical ablation to achieve optimal outcomes., (Copyright © 2013 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.)

Aim: To investigate the prevalence of clinically unrecognized mitral regurgitation (MR) in lone atrial fibrillation (AF)., Methods: We studied the prevalence and severity of MR by transesophageal echocardiography (TEE) in patients with "lone" AF as compared to a matched cohort of patients in normal sinus rhythm (NSR) undergoing TEE for other indications besides recognized valvular heart disease., Results: A total of 157 subjects (57 in the AF group and 100 in the NSR group) with structurally normal cardiac valves were included in the study. In the AF group, moderate MR or more was noted in 66% of the patients, mild MR in 18%, trace or no MR in 16%. In the control group, moderate MR was noted in 6% of patients, mild MR 31%, trace or no MR in 63 % of patients. Moderate MR or greater was significantly more prevalent in the AF group compared to the NSR group (66% vs 6%, P < 0.0001)., Conclusion: Clinically unrecognized moderate MR is prevalent in "lone" AF -either as an etiologic factor leading to "lone" AF or developing after onset of AF.

Atrial fibrillation (AF) is the most common sustained arrhythmia associated with increased morbidity and mortality. Efficacy and safety of currently employed antiarrhythmic drugs (AADs) continue to be less optimal in AF. Development of newer AADs has recently been made possible through a greater understanding of electro-pathophysiology of AF. Highly specific drugs acting on atria are currently being explored, although there is little data available on effectiveness of atrial specific agents in maintaining sinus rhythm. Combining AADs and non-AADs such as angiotensin converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase effectiveness of AADs in patients with AF. Controlled clinical trials are required to precisely define the efficacy of single agents versus various combinations in maintaining sinus rhythm in patients with AF. This review describes some of the most promising therapeutic approaches that may overcome some of the limitations of drugs used at present for the management of AF.