Irregular Heart Research Articles

An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease

IntroductionCardiovascular disease (CVD) is a leading cause of death and disability globally, with ischemia and arrhythmias being critical contributors. Ischemia, due to reduced myocardial blood flow, can lead to sudden cardiac death, while arrhythmias, marked by abnormal heart rhythms, are common in the elderly. Electrocardiography (ECG) is essential for the diagnosis of these conditions. This study aims to develop a clinically interpretable diagnostic framework for ischemia and arrhythmias using key ECG fiducial features. MethodsTo develop a robust ECG-based model for predicting cardiovascular diseases, we integrated data from three well-established ECG datasets: MIT-BIH Arrhythmia, European ST-T, and Fantasia. This aggregated dataset was employed to train multiple machine learning (ML) models aimed at automatically classifying heart conditions, including arrhythmia, ischemia, and healthy states. We designed a predictive framework utilizing boosting ML algorithms, enhanced by explainable artificial intelligence (XAI) techniques, to ensure high predictive performance in model interpretation. ResultsThe histogram gradient boosting classifier demonstrated superior classification performance, achieving an overall accuracy of 90 % in predicting heart disease based on ECG fiducial features. The model achieved area under the curve (AUC) scores of 0.99, 0.99, and 0.89 for the healthy, ischemic, and arrhythmic classes, respectively. XAI methods revealed that ECG fiducial features, such as the P-H, R-H, RR interval, QRS duration, QT interval, and ST segment, were significant diagnostic indicators for heart disease. ConclusionsThis study uses machine learning and XAI models to classify arrhythmia and ischemia from ECG data, enhancing interpretability clinical diagnostics for prevention and intervention to reduce disabilities.

Heartbeat classification using various machine learning models: A comparative study

Cardiac arrhythmias, known as irregular heartbeats, pose a notable health threat that necessitates prompt diagnosis, as untreated arrhythmias can lead to severe heart complications. Among the various methods for arrhythmia detection, electrocardiography is the most prevalent due to its non-invasive monitoring of heart activity. However, manual electrocardiogram (ECG) analysis is inefficient and prone to errors, prompting the exploration of machine learning (ML) models for ECG feature recognition. Integrating ML models with ECG analysis can revolutionize cardiac diagnostics by improving healthcare efficiency and outcomes by enhancing the accuracy and consistency of existing approaches as well as their processing speed for large datasets. Unfortunately, current ML methods encounter two key limitations: prolonged training times and the need for manual feature selection. To address these issues, we propose using ML models enhanced with innovative techniques such as the Fourier transform (FT) and Gaussian noise injection for improved cardiac health assessment. To validate this approach, we utilized statistical tools, including Pearson correlation and p-values, to uncover relationships within the data. In addition, we employed the FT technique to extract and analyze frequency-domain features. Our comparative study of different ML models relied on metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic area under the receiver operating characteristic curve, demonstrating XGBoost’s impressive average recall of 0.956 with 99.96% overall accuracy. An average precision of 0.956 further underscored the accuracy of XGBoost’s predictions, indicating its high level of reliability in distinguishing various cardiac conditions. These results highlight the considerable potential of ML techniques for precise ECG-based clinical diagnoses, helping healthcare professionals make more accurate and timely decisions in patient care.

Exposure to nicotine and withdrawal in Wistar rats: an electrophysiological study.

Throughout the world, smoking is one of the principal causes of preventable death. Nicotine, the primary active component of tabacco, acts as a psychostimulant and modulates the electrical activity of a number of the areas of the brain involved in addiction. Abstinence from nicotine will also impact the functional state of the brain, which is reflected in symptoms of craving and susceptibility to relapse. In addition, given the increase in the sympathetic tone of the heart and pulse rate promoted by nicotine, its consumption can contribute to tachyarrhythmia. The present study investigated the electroencephalographic (EEG) and electrocardiographic (ECG) patterns of Wistar rats submitted to acute or chronic exposure to nicotine, followed by withdrawal for 24 or 48 hours, and the re-administration (or not) of nicotine, to simulate episodes of relapse. The EEG data revealed an increase in all types of brainwaves, with emphasis on high-frequency (alpha, beta, and gamma) brain oscillations following both acute and chronic exposure to nicotine (14 days), whereas in withdrawal, there was a predominancy of delta waves. When exposure to nicotine was reinstated after withdrawal, the observed EEG profile was similar to that found in chronic exposure. The electrocardiogram reads showed that both acute and chronic exposure to nicotine caused abnormalities in the atrioventricular conduction and that, while these changes improve with substance withdrawal, relapse can worsen these parameters. The results of this study indicate that high-frequency brainwaves are correlated with nicotine dependence, while slow brain oscillations are consistent with drug craving, and episodes of nicotine relapse can reproduce brain activity patterns linked to dependence. Finally, exposure to nicotine predisposes the individual to heart rhythm abnormalities, which are attenuated by withdrawal, but may nevertheless be restored rapidly with re-exposure to the substance. This study demonstrated that nicotine increases high-frequency brain oscillations, which is associated with addiction, whereas withdrawal elevates the delta wave power, suggesting craving. Re-exposure to nicotine following withdrawal restores rapidly the EEG profile of chronic dependence. In addition, nicotine has deleterious impacts on cardiac activity, which are linked to fatal arrhythmias. This implies that stopping smoking is beneficial for the amelioration of the alterations in heart rhythm caused by nicotine addiction. This study elucidates the functional states of the brain and heart during both sporadic and chronic nicotine use, and the electrophysiological explanation for substance dependence and drug relapse after craving episodes.

Sinusoidal Atrial Fibrillation (Yasser’s Fibrillation) and Partial SAN Function in COVID-19 Pneumonia; a New Cardiovascular Discovery Change in Atrial Fibrillation Directory

Introduction: Atrial fibrillation (AF) is one of the most common and probable serious arrhythmias and a hallmark of an increasing risk of pathological thrombus formation. Interestingly, normal sinus rhythm (NSR) is the standard rhythm in a normal person. The sino-atrial node (SAN) is the heart's pacemaker and generator of the normal electrical conduction system of the heart which allows for the generating of electrical impulses. In AF, the normal regular electrical impulses generated by the sinoatrial node are overwhelmed by disorganized electrical waves, usually originating from the roots of the pulmonary veins. These disorganized waves conduct intermittently through the atrioventricular node (AVN) leading to irregular activation of the ventricles that generate the heartbeat. Cases presentation: Four different cases were described. 1. An elderly female patient presented to the ICU with metastatic complicated Mantle cell lymphoma, COVID-19 pneumonia, and irregular heart rate. 2. A senile male patient presented to the intensive care unit (ICU) with an aortic aneurysm, COVID-19 pneumonia, consumptive thrombocytopenia, lung fibrosis, and irregular heart rate. 3. A middle-aged female presented to the ICU with chronic renal failure (CRF) on regular hemodialysis, liver cell failure (LCF), pseudo-orange abdominal cellulitis, valvular heart replacement, and irregular heart rate. 4. An elderly female patient presented with cardiomyopathy, global cardiomegaly, irregular heart rate, diabetes, pseudo-orange abdominal cellulitis, liver cirrhosis, and COVID-19 pneumonia. Method of study and patients: Retrospective-Observational four case report series study was conducted in Kafr El-Bateekh Central Hospital staring from January 21, 2023 and ending on June 24, 2024 on COVID-19 pneumonia and atrial fibrillations. Conclusion: Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF are a new cardiovascular discovery. The partial sino-atrial nodal function has essential role in the presence of Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF and its interpretation. Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF may be balanced between AF and normal sinus rhythm. The percentages of normal sinus beats to AF beats in the cases of Sinusoidal atrial fibrillations (Yasser’s fibrillation) may be a guide for approximate healthy or sick part of the sinoatrial node. Widening the research for the Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF will be recommended.

Postmortem Identification of a Foreign Body in the Heart: A Case Report.

The presence of a foreign body in the heart have been reported, mostly in adults. Deaths and injuries can result from the breakage or rupture of angiography catheters. We present a case in which an angiocath tip was identified in the right ventricle of a pediatric patient, and the patient died as a result thereof. As no similar cases have been reported, we would like to draw attention to this subject. A 4.5-month-old female patient who was hospitalized 3 times during her life had a history of peripheral venous access establishment in the extremities with angiocatheters. During these hospital admissions, a central catheter was not inserted, and no additional problems were identified. Emergency personnel responded and found the patient at home, experiencing bradycardia and arrhythmia. She was resuscitated and taken to the hospital, where she died. The autopsy revealed, the tip of a 1.5cm long, 1mm wide lumen catheter had penetrated the right ventricular wall (Fig.1) (Fig.2), thus her death was attributed to complications that developed as a result of the peripheral vascular access catheter tip breaking and migrating to the right ventricle. Although the patient did not have an ECG, physical examination by the emergency ambulance personnel revealed an irregular and bradycardic heartbeat, suggesting that the child may have developed arrhythmia and atrioventricular conduction block. Health care professionals must check upon removal of peripheral intravenous access, and record whether the integrity of the peripheral catheter tip was compromised to prevent similar events in future. Fig.1 Angiocath tip piercing the right ventricular wall and exiting the heart Fig.2 a A 1.5cm long, b 1mm wide lumen catheter tip penetrating the right ventricular wall.

7342 Thyrotoxic Periodic Paralysis

Abstract Disclosure: G. Arora: None. Case presentation: A 40 year old male with no significant past medical history presented to the emergency department with diffuse body weakness for one day. On the night prior to admission, his thighs started getting sore which in a matter of a few hours, progressed to weakness involving his entire body. He was unable to flex his hips, shoulders, and neck. He was unable to get up or move without assistance. He was found to have an irregular heartbeat two weeks prior to admission and had undergone a thyroid ultrasound which showed inflammation of the thyroid gland. He was not started on any new medications. He endorsed fatigue, palpitations, neck swelling, and 45 lbs unintentional weight loss over 6 months. No fever, chills, vision changes, congestion, chest pain, shortness of breath, cough, tingling, numbness or weakness. He denied alcohol or drug use. Physical examination was notable for blood pressure 151/80 mm Hg, heart rate 120 bpm, enlarged thyroid gland tender to palpation, and muscle weakness (motor strength 3/5 in flexors and extensors of hips and knees). Labs were notable for K 2.3 (3.5 - 5.1 mmol/L), ALP 218 (32 - 91 U/L), TSH <0.010 (0.400 - 4.300 mcIU/mL), Free T4 3.5 (0.6 - 1.6 ng/dL), CK 608 (49 - 397 U/L). Urine toxicology was negative. MRI spine did not show any acute abnormality except for thyromegaly. He received potassium supplementation which led to symptomatic improvement and was admitted to medical floor. Further tests revealed: TSI 39.50 (<=0.54 IU/L), TSH receptor antibody 26 (<=1.75 IU/L), thyroid microsomal antibody 773.3 (0.0 - 9.0 IU/mL), anti-thyroglobulin antibody 4 (<4 IU/mL). Thyroid ultrasound showed an enlarged, heterogenous, hypervascular thyroid without any nodules. He was started on Propranolol and Propylthiouracil, which led to symptom resolution and he was discharged. Discussion: Thyrotoxic periodic paralysis (TPP) is a rare muscle disorder, belonging to the family of diseases called channelopathies. Thyroid hormone increases tissue responsiveness to beta-adrenergic stimulation, which increases Na-K ATPase activity on the skeletal muscle membrane driving potassium into cells. This leads to hyperpolarization of the muscle membrane and relative inexcitability of the muscle fibers, especially in the presence of a trigger such as provocation with exercise, fasting state or a high carbohydrate meal (insulin stimulates Na-K ATPase pump). Furthermore, more than 95% of TPP cases occur in males. This predominance is due to androgen-induced Na-K ATPase sensitization, more pronounced hyperinsulinemia, comparatively higher sympathetic and catecholamine-induced Na-K ATPase activity and higher muscle mass in males. Given the life-threatening association of severe hypokalemia, TPP should be considered in all patients presenting with acute painless muscle weakness. Presentation: 6/3/2024

A Comprehensive Review on Stereotactic Arrhythmia Radioablation (STAR): Pioneering a New Era in Arrhythmia Management.

Stereotactic arrhythmia radioablation (STAR) is an innovative treatment modality that leverages advanced imaging techniques and focused radiation to target arrhythmogenic foci within the heart, offering a non-invasive alternative to traditional catheter ablation methods. Arrhythmias, characterized by irregular heartbeats, pose significant health risks, including heart failure and stroke, particularly among older adults. Traditional management approaches, such as pharmacological therapies and catheter ablation, often face efficacy, safety, and recurrence limitations. STAR addresses these challenges by utilizing high-precision stereotactic technology to deliver targeted radiation, minimizing damage to surrounding tissues while maximizing therapeutic effects. This review explores the mechanisms, clinical indications, procedural techniques, and outcomes associated with STAR. Recent clinical studies demonstrate that STAR provides comparable efficacy to catheter ablation, with a favorable safety profile, making it a promising option for patients who have not responded to conventional treatments. Integrating STAR into arrhythmia management protocols may enhance patient outcomes and reduce the burden of recurrent arrhythmias. As research in this field advances, STAR holds the potential to reshape the landscape of arrhythmia treatment, providing new hope for patients suffering from these complex conditions. This review aims to elucidate the significance of STAR in modern arrhythmia management and to encourage further exploration and clinical application of this pioneering technique.

Effects of Ventricular Extrasystoles on Right Ventricle Functions (a Speckle Tracking Study). VES and RV Strain Imaging.

The adverse effects of ventricular extrasystoles (VES) on the heart, such as induced dyssynchrony, irregular heart rate, and atrioventricular dissociation, have been demonstrated. The aim of this study was to investigate the effects of VES on the right ventricle (RV) using strain imaging. Fifty patients with 5000 or more VES detected during 24hr Holterrhythm monitoring between April 2022 and September 2022 in the cardiology outpatient clinic were included in this study.A volunteer control group of 50 individuals matching the patients' age and demographic characteristics was selected. Right heart function parameters were compared echocardiographically between the two groups. In the VES group, both RV free wall strain (22.03±3.67, 29.52±3.01; p<0.001) and RV four-chamber strain (19.37±2.95, 22.34±2.11; p<0.001) were lower compared to the control groupIn the univariate regression analysis for decreased RV four-chamber strain, the presence of VES (p<0.001) was identified as a predictor, whereaas in the multivariate regression analysis, it was not considered to be an independent predictor. When evaluating the characteristics of the VES patients, the number of VES detected during Holter monitoring and delta QRS were observed as negative predictors of RV strain. This study demonstrated the adverse effects of VES on the right ventricle, as it is on the left ventricle.Therefore, regular monitoring of RV function with echocardiography is important in the follow-up of patients with VES.

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.

FPGA‐Based Implementation of Real‐Time Cardiologist‐Level Arrhythmia Detection and Classification in Electrocardiograms Using Novel Deep Learning

ABSTRACTCardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design.

Conventional Machine Learning and Ensemble Learning Techniques in Cardiovascular Disease Prediction and Analysis

Cardiovascular diseases, which significantly affect the heart and blood vessels, are one of the leading causes of death worldwide. Early diagnosis and treatment of these diseases, which cause approximately 19.1 million deaths, are essential. Many problems, such as coronary artery disease, blood vessel disease, irregular heartbeat, heart muscle disease, heart valve problems, and congenital heart defects, are included in this disease definition. Today, researchers in the field of cardiovascular disease are using approaches based on diagnosis-oriented machine learning. In this study, feature extraction is performed for the detection of cardiovascular disease, and classification processes are performed with a Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbor, Bagging Classifier, Random Forest, Gradient Boosting, Logistic Regression, AdaBoost, Linear Discriminant Analysis and Artificial Neural Networks methods. A total of 918 observations from Cleveland, Hungarian Institute of Cardiology, University Hospitals of Switzerland, and Zurich, VA Medical Center were included in the study. Principal Component Analysis, a dimensionality reduction method, was used to reduce the number of features in the dataset. In the experimental findings, feature increase with artificial variables was also performed and used in the classifiers in addition to feature reduction. Support Vector Machines, Decision Trees, Grid Search Cross Validation, and existing various Bagging and Boosting techniques have been used to improve algorithm performance in disease classification. Gaussian Naïve Bayes was the highest-performing algorithm among the compared methods, with 91.0% accuracy on a weighted average basis as a result of a 3.0% improvement.

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.

Fentanyl and Sudden Death-A Postmortem Perspective for Diagnosing and Predicting Risk.

Sudden, unexpected deaths are extremely difficult for families, especially when the victim is a child. Most sudden deaths occur due to cardiovascular issues, and a smaller number (approximately one-quarter) are attributed to other causes, such as epilepsy. The medicinal and non-medicinal use of the synthetic opioid fentanyl, which can cause breathing problems, is frequently involved in these deaths. It is also being found more often in autopsies of sudden death cases, and the number of overdose deaths from illicit drugs containing fentanyl is increasing. There are cases in which it is mixed with other drugs. A gene known as the KCNH2 gene or human ether-a-go-go-related gene (hERG), involved in the heart's electrical activity, can be related to abnormal heart rhythms. This gene, along with others, may play a role in sudden deaths related to fentanyl use. In response, we have examined the scientific literature on genetic variations in the KCNH2 gene that can cause sudden death, the impact of fentanyl on this process, and the potential benefits of genetic testing for the victims to offer genetic counseling for their family members.

The Correlation between Cardiac Magnetic Resonance Findings and Post-COVID-19: The Impact of Myocardial Injury on Quality of Life.

In the post-COVID-19 era, there is growing concern regarding its impact on cardiovascular health and the following effects on the overall quality of life of affected individuals. This research seeks to investigate cardiac magnetic resonance (CMR) findings following COVID-19 and their impact on the quality of life of affected individuals. An observational, cross-sectional study was conducted in consecutive patients with persistent cardiovascular symptoms after COVID-19 who were referred to CMR due to suspected myocardial injury. In addition, patients completed a questionnaire about symptoms and the quality of life during the post-COVID-19 period. In this study, 85 patients were included. The study population consisted of patients with a mean age of 42.5 ± 13.4 years, predominantly women, who made up 69.4% of the study population, while men made up 30.6%. CMR findings showed non-ischemic myocardial injury in 78.8% of patients and myocardial edema in 14.1% of patients. Late pericardial enhancement was present in 40% of patients and pericardial effusion in 51.8% of patients. Pericardial effusion (p = 0.001) was more prevalent in patients who reported more pronounced symptoms in the post-COVID-19 period compared to the acute infection phase. Predictors of lower quality of life in the post-COVID-19 period were the presence of irregular heartbeat (p = 0.039), cardiovascular problems that last longer than 12 weeks (p = 0.018), and the presence of pericardial effusion (p = 0.037). Acute myocarditis was observed in a minority of patients after COVID-19, while non-ischemic LGE pattern and pericardial effusion were observed in the majority. Quality of life was worse during the post-COVID-19 period in patients with CMR abnormalities, primarily in patients with pericardial effusion. Also, irregular heartbeat, cardiovascular symptoms that last longer than 12 weeks, as well as pericardial effusion were independent predictors of lower quality of life during the post-COVID-19 period.

O02 SIMULTANEOUS BLOOD PRESSURE AND ATRIAL FIBRILLATION SCREENING IN MAY MEASUREMENT MONTH 2022

Background and objective: May Measurement Month (MMM) is a global campaign aiming to raise awareness of raised blood pressure (BP). In 2022, concurrent screening for BP and atrial fibrillation (AF) was conducted in ten countries, given that hypertension and AF are both major treatable risk factors for stroke. Here, we evaluate AF prevalence and treatment by BP status among opportunistic samples of adults. Methods: In MMM, adults (≥18 years) undergo opportunistic BP screening. In ten participating countries, concurrent AF screening was carried out using OMRON Complete or M7 devices, in addition to three standardized, sitting BP measurements. ‘Known’ AF was defined as having had a previous diagnosis of either AF or irregular heartbeat, or taking oral anticoagulants. To risk-stratify those with AF, we calculated the CHA2DS2-VASc score. Hypertension was defined as a BP ≥140 and/or ≥90 mmHg (average of the second and third readings) and/or taking BP-lowering medication. Results: 80,751 adults (mean age 50.1 years; 58.2% female) underwent BP and AF screening. AF was detected in 1,526 (1.9%), of whom 1,014 (66.4%) had ‘known’ AF. Of those with ‘known’ AF, 214 (21.1%) were taking anticoagulants and 644 (63.5%) were taking neither anticoagulants nor aspirin. AF was detected more commonly among those aged ≥60 than <60 (3.2% vs 1.2%: p<0.001). Hypertension was detected more commonly among those with than without AF (63.6% vs 45.2%; age-sex-adjusted p<0.001). There was a graded association in anticoagulant and aspirin treatment rates according to CHA2DS2-VASc score in participants with ‘known’ AF (Table). Amongst participants with ‘known’ AF, anticoagulant usage was reported by 35.7% of those with a class 1 indication for anticoagulation according to CHA2DS2-VASc score. Conclusions Opportunistic AF screening in the MMM 2022 campaign detected AF in 3.2% of participants aged ≥60, and hypertension was more common in those with AF. Over one third of detected cases were not ‘known’ cases. Only 35.7% of ‘known’ cases with a class 1 indication for anticoagulants were treated.

Ventricular preexcitation of every other beat or ventricular bigeminy or both?

Bigeminy is a cardiac arrhythmia in which there is a single ectopic beat, or irregular heartbeat, following each regular heartbeat. This case demonstrates the diagnostic challenges that can be encountered while interpreting ECGs of patients with “apparent ventricular bigeminy’.

Abstract Tu107: Decreased Mitochondrial Protein Expression Accompanies Atrial Hypertrophy but Precedes Atrial Fibrillation Onset in a Mouse Model of Spontaneous Atrial Fibrillation

Introduction: Atrial fibrillation (AF), characterized by episodes of rapid and irregular heart rate, increases left atrial (LA) demand for ATP. While the heart is metabolically flexible, able to use fatty acids, lactate, glucose, ketones, and amino acids to generate ATP, fatty acids are the primary fuel source. AF is associated with altered mitochondrial structure and function, but the role of energetic pathways in early stages of AF progression is not clear. Hypothesis: Decreased expression of mitochondrial proteins contributes to AF progression. Aim: Evaluate expression of proteins that influence mitochondrial structure and energetic capacity in AF onset and progression. Methods: AF burden was assessed weekly by electrocardiography (ECG) in male wild-type (WT) and cAMP responsive element modulator IbΔC-X heterozygous mice (CREM) (n=3). LA (4 - 9 mg) was obtained at 7 weeks and 16 weeks. Global proteomics was conducted, and 139,316 peptides were identified that mapped to 6000 robustly quantified proteins. Results: LA hypertrophy was evident in 7-week and 16-week CREM mice (Figure 1). We detected 2408 proteins differentially expressed in CREM compared to WT. At 7 weeks, Hallmark pathways adipogenesis, fatty acid metabolism, oxidative phosphorylation, and myogenesis were decreased in CREM (Table 1). These changes persisted at 16 weeks. We identified 740 differentially expressed mitochondrial proteins using MitoCarta 3.0 (552 decreased, 188 increased). Mitochondrial pathways associated with energy production, including branched chain amino acid metabolism, fatty acid transport and oxidation, carbohydrate metabolism, and tricarboxylic acid cycle were decreased (Table 1). Mitochondrial cristae organizing system (MICOS) and mitophagy, pathways that influence mitochondrial structure and content, were decreased. Terminal ECGs confirmed AF in 16-week CREM mice only. The coefficient of variation of RR intervals (CVRR), a measure of heart rate variability, was increased in 16-week CREM (Figure 2). These data suggest that decreased expression of mitochondrial proteins accompanies LA hypertrophy but precedes onset of AF. Conclusions: Decreased expression of proteins that regulate mitochondrial structure and energy production prior to AF onset suggests that these proteins play an important role in early LA changes that promote AF onset and progression. Future studies will examine how these changes in protein expression relate to alterations in atrial energy homeostasis.

Abstract Mo074: Overexpression of IPP2 Impairs Intracellular Calcium Handling, Leading to Arrhythmogenic Events

Cardiac arrhythmias, which are disruptions in the heart's electrical activities, critically affect the ventricular conduction system, impairing efficient blood circulation. These disturbances result in irregular heartbeats, contributing to patient mortality or causing harm to distant organs via embolic events. Given the severe implications, a profound understanding of the molecular mechanisms driving arrhythmia is crucial for devising specific treatments. This study unveils a pioneering link between the overexpression of IPP2 (Protein Phosphatase 1 Regulatory Inhibitor Subunit 2), driven by the Myh6 promoter (termed TgIPP2), and the onset of age-dependent cardiac arrhythmias. The electrical abnormalities observed in TgIPP2 overexpressing mice include a spectrum of dysfunctions such as axis deviations, elongated QRS and PR intervals, absence of P waves, AV dissociation, ventricular tachyarrhythmia, and heightened T waves. Investigating the pathophysiology behind these electrical anomalies, we subjected young adult mice to catecholamine challenges at low doses and conducted surface electrocardiogram (EKG) monitoring. TgIPP2 mice demonstrated a notable increase in arrhythmogenic episode frequency and duration following 0.2 mg/kg isoproterenol injection compared to their control counterparts. Through a protein phosphorylation analysis, we identified a key regulator of the ryanodine receptor, which modulates calcium release from the sarcoplasmic reticulum, correlating with the extended PR intervals and the delayed peaking of T waves. Therapeutic trials employing RyR blockers, specifically dantrolene, successfully normalized the erratic cardiac rhythms in aged TgIPP2 mice. However, beta blockers or L-type calcium channel blockers induced sudden cardiac death in this model. The study reveals that IPP2-induced aberrant phosphorylation disrupts calcium homeostasis, leading to arrhythmias. In conclusion, our research marks a significant advancement in understanding arrhythmogenic mechanisms, demonstrating that IPP2 overexpression directly triggers arrhythmia, particularly affecting the atrioventricular (AV) node, atria, and ventricles. The discovery that aberrant phosphorylation, especially of the RyR regulator, plays a pivotal role in initiating cardiac arrhythmias opens new pathways for targeted anti-arrhythmic therapies.

Cardiac Arrhythmias and Their Management: An In-Depth Review of Current Practices and Emerging Therapies.

Cardiac arrhythmias encompass a range of conditions characterized by abnormal heart rhythms, affecting millions globally and significantly contributing to morbidity and mortality. This review provides a comprehensive analysis of the current practices and emerging therapies in managing cardiac arrhythmias, covering their definition, classification, epidemiology, and the critical importance of effective management. It explores the pathophysiology underlying various arrhythmias, including the mechanisms of arrhythmogenesis, such as re-entry, automaticity, and triggered activity. The review details the latest diagnostic tools, including ECG, Holter monitoring, and electrophysiological studies, and discusses the clinical presentation of different arrhythmias, from supraventricular to ventricular types and bradyarrhythmias. We examine current pharmacological and non-pharmacological treatment strategies, such as antiarrhythmic drugs, catheter ablation, and device therapy, highlighting their efficacy and limitations. Furthermore, the review delves into emerging therapies, including advanced catheter ablation techniques, novel antiarrhythmic agents, gene therapy, and innovative device technologies like leadless pacemakers and subcutaneous implantable cardioverter-defibrillators (ICDs). Special considerations for managing arrhythmias in diverse populations, including pediatrics, the elderly, and pregnant women, are discussed. Additionally, the review explores future directions in arrhythmia management, emphasizing personalized medicine, artificial intelligence applications, and the integration of advanced technologies in diagnosis and treatment. By synthesizing current knowledge and prospects, this review aims to enhance understanding and promote advancements in the field, ultimately improving patient outcomes with cardiac arrhythmias.

Cardiac SABR: Image matching techniques for accurate treatment delivery

BackgroundVentricular tachycardia is an irregular heartbeat conventionally treated using invasive cardiac catheter ablation and medication. However, when standard treatments have been exhausted, cardiac SABR provides a final treatment option to this high-mortality condition. Complex diagnostic mapping and planning scans enable multi-disciplinary target delineation for a 25Gy single fraction. However, organs at risk (OAR) near the target make this treatment challenging to plan and deliver. Publications from cardiologists report the efficacy of cardiac SABR, however there is limited data on the treatment delivery and image matching of this complex procedure. MethodsFour specialist therapeutic radiographers experienced in cardiac SABR reviewed 40 CBCTs from 10 patients treated in the UK. Each therapeutic radiographer conducted five image matches: a manual match (manual), an automatic match to the heart structure (auto) and the auto match followed by manual adjustment to the PTV (PTV), all using three degrees of freedom (DoF) only. The auto and PTV matches were also repeated using 6DoF. Inter-observer variability was quantified using 95% limits of agreement from a modified Bland-Altman analysis. ResultsThe limits of agreement were smallest in the automatic matches suggesting the algorithm is reliable. A manual adjustment from the auto match to the PTV is clinically appropriate to optimise target coverage. The limits of agreement were smaller in the 6DoF PTV match 1.06 mm, 1.24 mm, 1.68 mm than the 3DoF PTV match 1.57 mm, 2.06 mm, 2.11 mm (lateral, vertical, longitudinal). ConclusionThe 6DoF CBCT image match has less variability and therefore suggest using a 6DoF couch for treatment delivery. Implications for practiceCardiac SABR CBCT image matching at treatment delivery is complex, optimisation of CBCT acquisition parameters and therapeutic radiographer training is essential prior to implementation.