Type Of Arrhythmia Research Articles

Efficacy of percutaneous stellate ganglion block according to ventricular arrhythmia cycle length: a post-hoc sub-analysis of the STAR study

Abstract Background Percutaneous stellate ganglion block (PSGB) is an effective treatment for patients with electrical storm (ES). However, literature is scarce about a possible relation between ventricular arrhythmias’ cycle length and the effectiveness extent of PSGB. Purpose To assess whether the PSGB efficacy is affected by the arrhythmias’ cycle length prior to the procedure. Methods This is a post-hoc sub-analysis of the STAR study (STellate ganglion block for Arrythmic stoRm), a multicentre observational study aimed to assess PSGB’s effectiveness and safety in ES patients [1]. According to both the type and the median cycle of the latest ventricular arrhythmia before PSGB we considered 3 groups: ventricular fibrillation (VF), fast ventricular tachycardia (VT) and slowVT. The primary outcome is the number of treated arrhythmic episodes (with ATPs and/or DC-shocks) in the hour immediately after the PSGB as compared to the hour immediately before. The secondary outcome was the extent of the reduction of the number of ATPs/DC-shocks delivered from the hour before to the hour immediately after the PSGB. Results The original study included 184 PSGBs performed on 131 patients. We excluded 32 PSGBs with a continuous infusion and 13 PSGBs with an unknown VT cycle. The median VT cycle was 375 msec, so the final dataset of 139 PSGBs from 112 patients was divided as follows: 51 VF; 44 fastVT (VT cycle <375 msec) and 44 slowVT (VT cycle ≥375 msec). The number of treated arrhythmic episodes in the first hour after every PSGB was significantly lower compared to the first hour before in all the three groups [VF: 0 (IQR, 0-1) vs 5 (IQR, 2-8), p<0.001; fastVT: 0 (IQR, 0-0) vs 1 (IQR, 0-6.5), p<0.001; slowVT: 0 (IQR, 0-0) vs 1 (IQR, 0-4.5), p=0.001] (Figure 1). The Hodges-Lehman median difference of treated arrhythmic episodes between the hour after and before the PSGB was significantly higher in VF group [-4 (-3 to -6)] than in the fast-VT [-2 (-1 to -4)] and slow-VT [-1 (0 to -2.5)] (Figure 2). Analysing the reduction (delta less than zero) of the number of ATPs/DC-shocks from the hour before to the hour immediately after the PSGB, a significant trend was observed across the three groups (Jonckheere-Terpstra trend p<0.001) and a significant difference was observed comparing slow-VT vs VF [0 (IQR, -3 to 0) vs -4 (IQR, -7 to -1)] and fast-VT versus VF [-1 (IQR, -4.5 to 0) vs -4 (IQR, -7 to -1)], but not comparing slow-VT versus fast-VT [0 (IQR, -3 to 0) vs -1 (IQR, -4.5 to 0)]. VF was a predictor of the reduction of treated arrhythmic episodes at univariable analysis and after correction for the number of treated arrhythmic episodes in the hour immediately before PSGB and of left ventricular ejection fraction (OR 7.8, 95%CI 2.1-28.6). Conclusion PSGB is an effective treatment for ES in patients with all type of ventricular arrhythmias. However, its effectiveness was more pronounced in patients with VF or rapid ventricular tachycardia.

Predicting atrial fibrillation within 7 years follow-up using machine learning models

Abstract Background The prevalence of atrial fibrillation (AF), the most common type of cardiac arrhythmia, has increased globally. AF is also the leading cardiac factor for several cardiovascular and cerebrovascular events. Early prediction and treatment of AF prevents major cardiovascular and cerebrovascular events. Purpose Our objective is to predict AF risk within 7-years follow-up using only twelve basic factors and applying machine learning (ML) models. Methods Data from the German epidemiological trial on ankle brachial index (getABI) were utilized in this work. GetABI includes 6,457 patients with mean age of 72.53 years. 3,985 individuals were assessed for AF of which 393 patients suffered from AF at 7-years follow-up. The feature selection was initially done manually, ending up in 45 features. The 10 best features were extracted from this dataset according to the SHAP (SHapley Additive exPlanations) values. The final features were body mass index, sensitive CRP, cholesterol, LDL-cholesterol, troponin I, NT-pro-BNP, glomerular filtration rate, ratio of triglycerides and HDL-cholesterol, gamma-GT, heart rate, including age and sex as major factors. The handling of missing values was achieved by SimpleImputer, estimating the mean values. XGBoost and Random Forest predicted AF. Logistic Regression was also used as a baseline model. The data were split into training and test set and ML models were applied for 100 iterative runs, extracting results with mean values. Results XGBoost was the most accurate model, predicting the AF with 71.19 % mean accuracy, 66.37 % mean sensitivity and 75.61 % mean specificity. Figure 1 depicts the performance of all models. Additionally, Receiver Operating Characteristic Curve (ROC) and Area Under Curve (AUC) for each model are presented in Figure 2. XGBoost also outperformed all the others models, achieving the highest mean AUC value, equal to 70.99 %. Contrariwise, logistic regression had the lowest overall performance. Conclusion ML models, specifically XGBoost, achieved accurate AF prediction in 3,985 individuals utilizing only twelve clinical-routine data.Evaluation results of each used model.ROC curve and AUC value of each model.

A multi-center retrospective cohort analysis of age and racial disparities in patients undergoing repeat ablation for recurrent atrial fibrillation

Abstract Introduction Atrial fibrillation (AF) is the most common type of cardiac arrhythmia, and its prevalence has been increasing worldwide. Current data shows that AF is more common in white males compared to blacks despite blacks having multiple cardiovascular comorbidities. There is paucity of data showing the recurrence of AF re-ablation after index ablation specifically as it pertains to race, gender, and age. We sought to explore the rates of recurrent AF re-ablation in this retrospective cohort analysis. Methods Using a multi-center database, we examined 23,558 encounters and 10,530 had readmission records i.e. readmitted within 3 years. The encounters included adults (18+) who had an ablation procedure for atrial fibrillation between 1/1/2018-06/30/2020. We used binary logistic regression models and Tukey-Kramer procedure to analyze pairwise comparisons among different subgroups of race, sex, and age group. The primary endpoint was recurrence of repeat ablation. Secondary endpoints included non-fatal stroke and myocardial infarction. Results Our sample size was 23,558 patients, 20,276 were white patients (86%) and 3,282 were non-white patients (14%). The average age was 66.29 years (SD 10.43) and average age at the time of repeat ablation was 65.97 years. The average BMI was 21.55. Out of the 23,558 patients, 14.30% (N= 3368) had re-ablation within 3 years of the index ablation. The odds ratio of re-ablation for patients in their 50s is 1.4229 (95% CI [1.1172, 1.8122]) compared to patients in their 80s. Similarly, the odds ratio is 1.4330 (95% CI [1.1466, 1.7909]) for the 60-69 group, 1.3075 (95%CI [1.0458, 1.6346]) for the 70-79 group and 1.3443 (95% CI [1.0088, 1.7914]) for patients below 50 compared to over 80 group. The LR for re-ablation was 0.8438 (p=0.3583) in females. The incidence of non-fatal stroke and MI were 0.27% (N= 63) and 0.19% (N= 45). Average length of time to re-ablation was 415.5 days. Discussion When controlling for age group and sex, race was not significantly associated with an increased likelihood of repeat ablation. When controlling for race and age group, sex was not significantly associated with an increased likelihood of repeat ablation. When controlling for race only, females were not at a higher risk of re-ablation. However, when controlling for race and sex, patients in their 50s are 42.3% more likely to have re-ablation when compared to patients in their 80s. In contrast, the age of patients at their first ablation increased, there appeared to be a rising trend indicating a higher chance of AF recurrence within 3 years when controlling for race and sex. Conclusion The incidence of repeat AF ablation is not higher in racial minorities when compared to whites. Interestingly, there appears to be an decreasing rate of AF re-ablation in older patients despite a higher rate of AF recurrence as age increases possibly owing to more conservative therapeutic approach such as medical management.

Cardiac arrhythmias in fish induced by natural and anthropogenic changes in environmental conditions.

A regular heartbeat is essential for maintaining the homeostasis of the vertebrate body. However, environmental pollutants, oxygen deficiency and extreme temperatures can impair heart function in fish. In this Review, we provide an integrative view of the molecular origins of cardiac arrhythmias and their functional consequences, from the level of ion channels to cardiac electrical activity in living fish. First, we describe the current knowledge of the cardiac excitation-contraction coupling of fish, as the electrical activity of the heart and intracellular Ca2+ regulation act as a platform for cardiac arrhythmias. Then, we compile findings on cardiac arrhythmias in fish. Although fish can experience several types of cardiac arrhythmia under stressful conditions, the most typical arrhythmia in fish - both under heat stress and in the presence of toxic substances - is atrioventricular block, which is the inability of the action potential to progress from the atrium to the ventricle. Early and delayed afterdepolarizations are less common in fish hearts than in the hearts of endotherms, perhaps owing to the excitation-contraction coupling properties of the fish heart. In fish hearts, Ca2+-induced Ca2+ release from the sarcoplasmic reticulum plays a smaller role than Ca2+ influx through the sarcolemma. Environmental changes and ion channel toxins can induce arrhythmias in fish and weaken their tolerance to environmental stresses. Although different from endotherm hearts in many respects, fish hearts can serve as a translational model for studying human cardiac arrhythmias, especially for human neonates.

Effect of Spatial Resolution on Accurate Detection and Localization of Arrhythmia Rotors in Human Right Ventricular Tachycardia.

The goal of this study was to identify the spatial resolution requirements for accurate rotor detection and localization in human right ventricular tachyarrhythmias. Poor spatial resolution is often cited as a reason for the inaccuracy of cardiac mapping catheters in detecting and localizing arrhythmia rotors. High-resolution (0.7 mm) arrhythmia data from optical recordings obtained from human donor hearts (n = 12) were uniformly downsampled to lower resolutions (1.4-7 mm) to approximate the spatial resolution (4 mm) of clinical mapping catheters. Rotors were tracked at various subresolutions and compared to the rotors in the original data by computing F1-scores to create accuracy profiles for both rotor detection and localization. Further comparisons were made according to arrhythmia type, donor sex, anatomical region, and mapped surface: endocardium or epicardium. For a spatial resolution of 4.2 mm, the accuracies of rotor detection and localization were 57% ± 4% and 61% ± 7%, respectively. Arrhythmia type affected the accuracy of rotor detection (monomorphic ventricular tachycardia, 58% ± 4%; ventricular fibrillation, 56% ± 8%) and localization (monomorphic ventricular tachycardia, 70% ± 4%; ventricular fibrillation, 54% ± 13%). However, donor sex, anatomical region (right ventricular outflow tract, mid, and apical), and mapped surface (epicardium and endocardium) did not significantly affect rotor detection or localization accuracy. To achieve rotor detection accuracy of 80%, a spatial resolution of 1.4 mm or better is needed. The accuracy profiles provided here serve as a guideline for future mapping device development.

Causal association between common rheumatic diseases and arrhythmia: a Mendelian randomization study.

Observational studies have suggested a link between rheumatic diseases and arrhythmias. However, these studies have been limited by confounding factors and reverse causality, leaving the causal relationship between rheumatic diseases and arrhythmias uncertain. This study addresses this inquiry using genetic evidence. Selected single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) data were employed as instrumental variables. Inverse variance weighting (IVW), MR-Egger regression, and the weighted median method were utilized in the two-sample Mendelian randomization analysis. Horizontal pleiotropy was identified and rectified through the MR-PRESSO test and MR-Egger regression. The stability and reliability of the Mendelian randomization results were appraised using the remain-one method, Cochran Q-test, and funnel plot. Odds ratios (OR) were utilized to assess the causal relationship between six rheumatic diseases and five types of arrhythmias. The Inverse Variance Weighted (IVW) method indicated a significant association between rheumatoid arthritis (RA) and an elevated risk of right bundle branch block (RBBB) (OR: 1.10, 95% CI: 1.02-1.18, p = 0.009). Additionally, gout was significantly correlated with an augmented risk of RBBB (OR: 1.28, 95% CI: 1.09-1.51, p = 0.003). Conversely, dermatomyositis (DM) exhibited a negative association with the risk of atrioventricular block (AVB) (OR: 0.94, 95% CI: 0.90-0.99, p = 0.020). No significant associations were observed between other rheumatic diseases and arrhythmias. A two-sample Mendelian Randomization (MR) study provides data indicating that in European populations, a genetically predicted gout or rheumatoid arthritis (RA) may increase the incidence of right bundle branch block (RBBB). To clarify and investigate the processes behind these causal links, more research is necessary. Because racial genetic variability exists, care should be used when interpreting our findings.

Characterization of atrial arrhythmias following mitral valve repair: Incidence and risk factors.

This study aims to investigate the occurrence, type and correlation of early and late atrial arrhythmias following mitral valve repair in patients with no preoperative history of atrial arrhythmias. Patients undergoing mitral valve (MV) repair for degenerative disease were included. Early and late postoperative electrocardiograms were evaluated for the incidence and type of atrial arrhythmia (atrial fibrillation [AF] or atrial tachycardia [AT]). The 192 patients were included. Early atrial arrhythmias occurred in 100/192 (52.1%) patients; AF in 61 (31.8%) patients, early AT in 15 (7.8%) and both in 24 (12.5%). In total 89% of patients were discharged in sinus rhythm. During a follow-up time of 7.3 years, 14 patients (7.3%) died and 49 (25.5%) patients developed late atrial arrhythmias. At 10 years, the cumulative incidence of any late atrial arrhythmia, with death as competing risk, was 64% (95% confidence interval [CI] = 55%-72%). On Fine-Gray model analysis, only early postoperative AF lasting >24 h was related to the development of late AF (hazard ratio 5.99, 95% CI = 1.78%-20.10%, p = .004). Early postoperative ATs were related to the development of late tachycardias, independent of their duration (<24 h hazard ratio 4.25, 95% CI = 1.89-9.57, p = .001 and >24 h hazard ratio 3.51, 95% CI = 1.65-7.46, p = .001). Early and late atrial arrhythmias were common after MV repair surgery. Only early postoperative AF lasting >24 h was a risk factor for the occurrence of late AF. Conversely, any postoperative AT was correlated to the development of late ATs.

MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Arrhythmias, also known as irregular heartbeats, are important health problems that must be accurately identified to diagnose and treat cardiovascular disease. Within the scope of this study, a network for classifying arrhythmias, which are important in the diagnosis and treatment of cardiovascular diseases, was proposed by using one-dimensional convolutional neural network (1D CNN), one of the deep learning techniques. With the proposed 1D-CNN architecture, arrhythmia types and normal rhythm ECGs were subjected to a more detailed examination from general to specific according to urgency situations. In the classifications made, first of all, a binary classification was made and an evaluation was made as whether there was a life risk or not. In triple, quadruple and six-fold classification, the detection of arrhythmia status is detailed. More complex classifications have helped to define different types of arrhythmias in more detail. This study proposes a deep learning network for automatic identification and classification of arrhythmias and shows that different arrhythmia conditions can be diagnosed with a single network model by applying the proposed network structure to multi-class arrhythmia disorders.

Enhancing Cardiovascular Health with the Implementation of Digitalis

Cardiovascular diseases (CVDs) are a major global health problem that cause a lot of illness, death, and high healthcare costs. As medical science advances, the number of people with CVDs continues to rise, necessitating the development of new methods for their prevention, diagnosis, and treatment. In recent years, using digital health technologies in cardiovascular care has become an excellent way to improve patient outcomes and make disease management better. It is possible for digitalis medicines, which come from the foxglove plant (Digitalis purpurea), to improve heart health in the digital age. In the field of cardiovascular medicine, this in-depth review looks at digitalis's historical importance, pharmacology, clinical uses, safety concerns, and digital transformation. The review starts with an overview of the epidemiological setting and the problems that CVDs cause, emphasizing the urgent need for creative solutions to deal with this worldwide health event. This article deals with how it has changed over time, from herbal remedies to modern drug therapy. It informs about the pharmacology of digitalis, mostly the way it works, its pharmacokinetics, and its pharmacodynamics. Pharmaceuticals that contain digitalis mainly work by stopping the Na+/K+ ATPase pump. The medication can help with heart failure, atrial fibrillation, and some types of arrhythmias. Digitalis's digital transformation is looked at with a focus on the way digital health technologies are used in its management, monitoring, and effectiveness. The review highlights that digitalis-based interventions could change the way cardiovascular medicine is done in the modern era.

Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia, with catheter ablation being a key alternative to medical treatment for restoring normal sinus rhythm. Despite advances in understanding AF pathogenesis, approximately 35% of patients experience AF recurrence at 12 months after catheter ablation. Therefore, accurate prediction of AF recurrence occurring after catheter ablation is important for patient selection and management. Conventional methods for predicting post-catheter ablation AF recurrence, which involve the use of univariate predictors and scoring systems, have played a supportive role in clinical decision-making. In an ever-changing landscape where technology is becoming ubiquitous within medicine, cardiac imaging and artificial intelligence (AI) could prove pivotal in enhancing AF recurrence predictions by providing data with independent predictive power and identifying key relationships in the data. This review comprehensively explores the existing methods for predicting the recurrence of AF following catheter ablation from different perspectives, including conventional predictors and scoring systems, cardiac imaging-based methods, and AI-based methods developed using a combination of demographic and imaging variables. By summarising state-of-the-art technologies, this review serves as a roadmap for developing future prediction models with enhanced accuracy, generalisability, and explainability, potentially contributing to improved care for patients with AF.

Factors associated with stellate ganglion block success in recurrent ventricular arrhythmias.

One treatment option for refractory ventricular arrythmias is stellate ganglion block (SGB). We examined differences in SGB success by patient and arrhythmia characteristics and predictors of successful SGB. This was a multicenter analysis of patients treated for refractory ventricular arrythmias in the Czech Republic and the United States. The primary outcome was absence of ventricular arrythmias at 24h post SGB. SGB effectiveness was examined according to aetiology of cardiomyopathy, arrhythmia type, laterality of SGB, presence of inotropes, and presence of mechanical circulatory support. Binary logistic regression was used to examine variables associated with the primary outcome. In total there were 117 patients with refractory ventricular arrythmias treated with SGB. Overall, the mean age was 63.5±11.0years, majority of patients were male (94.0%), White (87.2%), and had an implantable cardioverter defibrillator in situ (70.1%). There were no differences in efficacy of SGB based on aetiology of cardiomyopathy (P=0.623), arrhythmia type (0.852), laterality of block (P=0.131), and presence of inotropes (P=0.083). Multivariable analysis demonstrated that increased age was associated with decreased odds of SGB success (odds ratio: 0.96, confidence interval: 0.92-0.99, P=0.039) whereas increased left ventricular ejection fraction trended towards increased odds of SGB success (odds ratio: 1.05, confidence interval: 0.995-1.11, P=0.077). In this multicentre experience, SGB was similarly effective despite the aetiology of cardiomyopathy, type of arrhythmia, laterality, and inotropic or mechanical support. SGB was less effective for the suppression of ventricular arrythmias at 24h for the elderly.

The Japanese Catheter Ablation Registry (J-AB): Annual report in 2022.

The Japanese Catheter Ablation (J-AB) registry, started in August 2017, is a voluntary, nationwide, multicenter, prospective, observational registry, performed by the Japanese Heart Rhythm Society (JHRS) in collaboration with the National Cerebral and Cardiovascular Center. From January 2022, the data registration system was changed from Research Electronic Data Capture (REDCap) system to Fountayn system. The purpose of this registry was to collect the details of target arrhythmias, the ablation procedures, including the type of target arrhythmias, outcomes, and acute complications in the real-world settings. During the year of 2022, we have collected a total of 90,042 procedures (mean age of 66.7 years and 65.9% male) from 614 participant hospitals. Detailed data were shown in Figures and Tables.

Associated Factors and Mortality of Arrhythmia in Emergency Department: A Narrative Review.

Cardiac arrhythmias represent a major concern in the emergency department (ED), particularly given their association with significant morbidity and mortality. This narrative review examines the various factors influencing arrhythmias and their impact on patient outcomes in emergency settings. Managing complex supraventricular and ventricular arrhythmias (VAs) during acute myocardial infarction (AMI) and severe cardiovascular conditions remains challenging, despite advancements in diagnostic and therapeutic techniques. Ventricular arrhythmias frequently forecast worse outcomes during hospital stays and heighten the chances of sudden cardiac death and cardiac arrest, especially within the initial 30 days after a heart attack. The incidence of arrhythmias in ED is increasing due to demographic changes and higher rates of chronic illnesses such as diabetes, hypertension, and chronic kidney disease. These comorbidities, coupled with lifestyle factors such as smoking and alcohol consumption, complicate arrhythmia management, especially among older adults and males. Rapid and precise ECG interpretation in the ED is crucial for identifying specific arrhythmia types and initiating appropriate treatments. Atrial fibrillation (AF), the most prevalent form of rapid heart rhythm originating above the ventricles, notably impacts patient outcomes, particularly in cases of AMIand heart failure. In the ED, managing AFfocuses on preventing strokes with thromboprophylaxis and employing risk assessment tools such as CHA2DS2-VASc and HAS-BLED scores. The analysis highlights how risk factors like hypertension, obesity, obstructive sleep apnea (OSA), and diabetes intricately influence the development and worsening of AF. Optimizing AF treatment outcomes requires a multidisciplinary approach involving cardiologists, emergency physicians, and critical care specialists. Future research should prioritize evaluating the effectiveness of preventive and therapeutic interventions for AF, integrating new risk factors and genetic insights to enhance prediction and management strategies. Understanding the factors contributing to arrhythmias and mortality in the ED underscores the importance of timely and accurate diagnostic and therapeutic measures to improve patient care and outcomes.

A deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes

BackgroundDetection of atrial tachyarrhythmias (ATA) on long-term ECG recordings is a pre-requisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep Learning (DL) algorithms provide improved performances on resting ECG databases. However, results on long-term Holter recordings are scarce. ObjectiveWe aimed to build and evaluate a DL modular software using ECG features well known to cardiologists with a user interface that allows cardiologists to adjudicate the results and drive a second DL analysis. MethodsUsing a large (n=187 recordings, 249,419 one-minute samples), beat-to-beat annotated, two-lead Holter database, we built a DL algorithm with a modular structure mimicking expert physician ECG interpretation to classify atrial rhythms. The DL network includes 3 modules (cardiac rhythm regularity, electrical atrial waveform, and raw voltage by time data) followed by a decision network and a long-term weighting factor . The algorithm was validated on an external database. ResultsF1 scores of our classifier were 99% for ATA detection, 95% for Atrial fibrillation and 90% for Atrial flutter respectively. Using the external MIT-database, the classifier obtains F1-score of 97% for the NSR class and 96% for the ATA class. Residual errors could be corrected by manual deactivation of one module in 7/15 of the recordings with an accuracy < 90%. ConclusionA DL modular software using ECG features well known to cardiologists provided an excellent overall performance. Clinically significant residual errors were most often related to the classification of the atrial arrhythmia type (fibrillation vs. flutter). The modular structure of the algorithm helped to edit and correct the AI-based first-pass analysis and will provide a basis for explainability.

Risk Factors for Postoperative Atrial Fibrillation in Myocardial Revascularization Surgery: A 15-Year Experience.

Background: Myocardial revascularization surgery (MRV) is a revascularization therapy for coronary artery disease aimed at improving survival conditions. Elderly patients with increased comorbidities undergoing MRV face challenges in preventing postoperative complications, including atrial fibrillation (AF), a common arrhythmia occurring in 40% of cases or even in 80% of cases if the procedure is combined with valve surgery. This study aimed to determine the risk factors associated with the appearance of postoperative AF (POAF) in patients undergoing isolated MRV. Methods: This is an epidemiological, retrospective, and analytical case-control study (90 cases and 360 controls). Results: Mortality within the group of patients who presented with POAF in the study population was 15.5%, and 9.44% in the control group. Logistic regression showed an association of AF with the presurgical variables age >60 years and urgent/emergency surgery and the postsurgical variables cardiogenic shock, blood transfusion, pulmonary edema, pleural effusion, orotracheal reintubation, and mechanical ventilation time. Conclusions: Strategies should be proposed for the timely identification of risk factors and postoperative complications related to AF onset to avoid the increased morbidity and mortality associated with this type of arrhythmia during the postoperative period.

Rat Model of Right-Sided Cardiac Remodeling and Arrhythmia using Pulmonary Artery Banding.

Clinical conditions, including chronic obstructive pulmonary disease or pulmonary arterial hypertension (PAH), can lead to chronic right ventricle pressure overload and progressive right heart failure (RHF). RHF can be identified by right-sided cardiac hypertrophy and dilation associated with abnormal myocardial function affecting the RV and the right atrium (RA). We recently demonstrated that severe RHF is accompanied by an increased risk of atrial inflammation, atrial fibrosis, and atrial fibrillation (AF), the most common type of cardiac arrhythmia (CA). Recent studies have shown that RV and RA inflammation plays an important role in the arrhythmogenesis of CA, including AF. However, the impact of inflammation in the development of CA and AF in RHF is poorly described. Experimental models of RHF are required to better understand the association between right-sided myocardial inflammation and CA. The rat model of monocrotaline (MCT)-induced pulmonary hypertension (PH) is well-established to provoke RHF. However, MCT triggers severe pneumo-toxicity and pulmonary inflammation. Hence, MCT-induced RHF does not help to distinguish whether the subsequent myocardial inflammation originates from the RHF per se or circulating inflammatory signals secreted by the injured lung. In this article, a mechanical method involving pulmonary artery trunk banding (PAB) was used to provoke right-sided cardiac arrhythmogenesis. The PAB consists of performing a permanent suture of the pulmonary artery trunk for 3 weeks. Such an approach generates increased right-sided pressure overload. At D21 post-PAB, the suture results in hypertrophied, dilated, and inflamed RV and RA. The PAB-induced RHF is also accompanied by vulnerability to ventricular and atrial arrhythmias, including AF.

XDTEncoder: A Deep Explainable Arrhythmia Classification Framework for Smart Healthcare

In the context of smart healthcare, the integration of multimedia and digital twin technologies has driven significant advances in telemedicine. Electrocardiogram (ECG) signals, as a part of multimedia healthcare data, provide crucial digital information about the electrical activity of the heart, which is essential for diagnosing arrhythmias and ensuring a healthy life. Arrhythmia classification is a fundamental step in analyzing ECG signals and a critical problem for diagnosing heart diseases. One key challenge in arrhythmia classification is the lack of high accuracy for classifying arrhythmia heartbeats, and another key challenge is the lack of interpretability of decision-making models. This study aims to develop a novel approach to improve the performance of arrhythmia classification while providing explainable diagnostic decision paths. We propose XDTEncoder, an explainable arrhythmia classification framework that leverages multi-level features to classify arrhythmia heartbeats while offering an explainable diagnostic decision path. XDTEncoder is novel in three aspects. (1) It constructs a human-machine collaborative knowledge representation based on the Encoder-Decoder paradigm, which allows our model to classify arrhythmias while producing decision paths for cardiologists. (2) XDTEncoder compares two encoding methods (the binary tree encoding method and the Huffman encoding method) to embed the diagnostic decision tree into the arrhythmia classification framework. (3) XDTEncoder fuses multi-level features to improve the performance of arrhythmias classification. Evaluation on 5 types of arrhythmias in the MIT-BIH database demonstrates that our new approach outperforms state-of-the-art classifiers while providing interpretability.

The relationship between serum potassium levels and cardiac arrhythmias in left ventricular assist device (LVAD) recipients: a comprehensive analysis and prognostic evaluation.

This study aimed to comprehensively analyze the relationship between serum potassium (K+) levels and the risk of de novo cardiac arrhythmias in left ventricular assist device (LVAD) recipients. We performed a retrospective study using the INTERMACS registry. Data was collected on adult patients with available K+ measurements taken 1-month post-LVAD implantation. K+ levels were the main exposure of interest and were analyzed as a continuous and categorical variable (quartiles of baseline K+ distribution). The main outcome of interest was the occurrence of de novo arrhythmia events, either sustained (ventricular [VA] or supraventricular arrhythmia [SVA]) or not sustained (atrial fibrillation/flutter [AF]). All-cause mortality was evaluated as the secondary outcome. Multivariable adjusted time-dependent Cox regression models and natural splines were used to describe the relationship between the exposure and outcomes of interest. 10,570 patients met our inclusion criteria. A significant and consistent relationship was observed between the lowest quartile of longitudinal K+ and the risk of arrhythmic events (HR 1.28, 95% CI 1.08, 1.53, p = 0.005) as well as in the highest K+ quartile (HR 1.24, 95% CI 1.02, 1.49, p = 0.027). A similar relationship was confirmed in the stratified analysis of arrhythmia types for SVAs and AF. The data were reflected in a U shaped relationship. Similarly, the highest and lowest quartiles of longitudinal K+ were independently associated with a significant increase in the HR of death, which was reflected by a U shaped relationship. Our study reveals a significant U shaped relationship between low and high K + levels and cardiac arrhythmias in LVAD patients, particularly SVAs and AF. Both high and low K + levels negatively impacted patient survival.

An Enhancement for Wireless Body Area Network Using Adaptive Algorithms

Wireless Body Area Networks (WBANs) are one of the most critical technologies for maintaining constant monitoring of patient’s health and diagnosing diseases. They consist of small, wearable wireless sensors transmitting signals. Within this vision, WBANs are not without unique difficulties, for instance, high energy consumption, heat from the sensor, and impaired data accuracy. This paper introduces adaptive algorithms combining Convolutional Neural Networks (CNNs) and dynamic threshold mechanisms to enhance the performance and energy efficiency of Wireless Body Area Networks. The study utilizes the MIB-BIH Arrhythmias dataset to improve the detection of arrhythmias. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. As a result, the model reached the average accuracy of ECG classification of 98% and a high level of selectivity and sensitivity to a normal type of heartbeat and quite satisfactory results in the classification of arrhythmia type of heartbeat.

Enhancing boundary detection of radiofrequency ablation lesions through photoacoustic mapping

Atrial fibrillation (A-fib) is the most common type of heart arrhythmia, typically treated with radiofrequency catheter ablation to isolate the heart from abnormal electrical signals. Monitoring the formation of ablation-induced lesions is crucial for preventing recurrences and complications arising from excessive or insufficient ablation. Existing imaging modalities lack real-time feedback, and their intraoperative usage is in its early stages. A critical need exists for an imaging-based lesion indexing (LSI) method that directly reflects tissue necrosis formation. Previous studies have indicated that spectroscopic photoacoustic (sPA) imaging can differentiate ablated tissues from their non-ablated counterparts based on PA spectrum variation. In this paper, we introduce a method for detecting ablation lesion boundaries using sPA imaging. This approach utilizes ablation LSI, which quantifies the ratio between the signal from ablated tissue and the total tissue signal. We enhance boundary detection accuracy by adapting a regression model-based compensation. Additionally, the method was cross-validated with clinically used intraoperative monitoring parameters. The proposed method was validated with ex vivo porcine cardiac tissues with necrotic lesions created by different ablation durations. The PA-measured lesion size was compared with gross pathology. Statistical analysis demonstrates a strong correlation (R > 0.90) between the PA-detected lesion size and gross pathology. The PA-detected lesion size also exhibits a moderate to strong correlation (R > 0.75) with local impedance changes recorded during procedures. These results suggest that the introduced PA imaging-based LSI has great potential to be incorporated into the clinical workflow, guiding ablation procedures intraoperatively.