Frequency of cardiac arrhythmias in older adults: Findings from the Subclinical Atrial Fibrillation and Risk of Ischemic Stroke (SAFARIS) study

Left Atrial Strain and Incident Atrial Fibrillation in Older Adults

Atrial fibrillation (AF) is frequent in older adults and associated with left atrial (LA) dysfunction. LA strain (LAε) and LA strain rate (LASR) may detect subclinical LA disease. We investigated whether reduced LAε and LASR predict new-onset AF in older adults without history of AF or stroke. LAε and LASR were assessed by speckle-tracking echocardiography in 824 participants from the community-based Cardiovascular Abnormalities and Brain Lesions study. Positive longitudinal LAε and LASR during ventricular systole, LASR during early ventricular diastole, and LASR during LA contraction were measured. Cause-specific hazards regression model evaluated the association of LAε and LASR with incident AF, adjusting for pertinent covariates. The mean age was 71.1 ± 9.2 years (313 men, 511 women). During a mean follow-up of 10.9 years, new-onset AF occurred in 105 participants (12.7%). Lower LAε and LASR at baseline were observed in patients with new-onset AF (all p <0.01). In multivariable analysis, positive longitudinal LAε (adjusted hazard ratio [HR] per SD decrease 2.05, confidence interval [CI] 1.24 to 3.36) and LASR during LA contraction (HR per SD increase 2.24, CI 1.37 to 3.65) remained associated with new-onset AF, independently of LA volumes and left ventricular function. Along with positive longitudinal LAε, reduced LASR during ventricular systole predicted AF in participants with LA volume below the median value (HR 2.54, CI 1.10 to 6.09), whereas reduced LASR during LA contraction predicted AF in participants with larger LA (HR 2.35, CI 1.31 to 4.23). In conclusion, reduced positive longitudinal LAε and LASR predict new-onset AF in older adults regardless of LA size and may improve AF risk stratification.

Exercise-induced atrial fibrillation: A case report

Key Clinical Message

Middle-aged male athletes, with or without underlying coronary artery disease, exhibiting exercise induced blood pressure (BP) variability and diabetes can have an increased risk of developing atrial fibrillation (AF). Assessment in athletes should include long-term arrhythmia monitoring. In addition, it is important to exert patients beyond their calculated target heart rate (HR) during an exercise stress test to detect exercise-induced AF. We suggest this strategy be specifically used for athletes with complaints of intermittent palpitation and chest pain. Referral to an electrophysiologist for a possible ablation procedure should be considered for the management of AF in athletes in whom the use of beta-blockers may limit exercise tolerance. Bleeding risk with the use of oral anticoagulation needs to be adequately evaluated in athletes with AF who engage in high-intensity exercise or activities.

Abstract

The report highlights the case of a 54-year-old Caucasian male (height 5.11', BMI 29.8) who presented with complaints of chest pain, mild coronary artery disease, palpitation, dizziness, and labile BP with high-intensity biking exercise. Diagnostic tests (exercise stress test, cardiac catheterization, Holter monitor, and Bardy patch) using standard procedure were unsuccessful at detecting the problem. In a repeat exercise stress test, the patient was exerted beyond the calculated HRmax (up to 117%) when the patient's heart rhythm flipped from sinus rhythm to AF. The patient was referred to a cardiac electrophysiologist and an ablation procedure was performed to prevent exercise-induced AF with high-intensity exercise. Young adults, with or without early coronary artery disease, performing high-intensity endurance exercises may be at risk of developing exercise-induced AF. This phenomenon is prevalent and well documented in the skiing population and patients with variance in BP during exercise. Endurance athletes tend to have a lower resting HR. As such, the use of standard rate-control medications in patients with exercise-induced AF may not be appropriate. Referral to a cardiac electrophysiologist and ablation procedures should be considered in this population for management and symptom control. If tolerated, especially in young adults with complaints of palpitation and chest pain, patients should be exerted beyond their calculated HRmax during an exercise stress test to diagnose an underlying condition of exercise-induced AF.

Ensemble classifier fostered detection of arrhythmia using ECG data

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).

Subclinical Left Ventricular Dysfunction and Ventricular Arrhythmias in Older Adults With Normal Ejection Fraction

Background Premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) are known to be associated with reduced left ventricular (LV) ejection fraction and adverse outcomes in patients with structural heart disease. The relationship between subclinical LV dysfunction and ventricular arrhythmias in the general population is not established. Methods and Results Participants in the SAFARIS (Subclinical Atrial Fibrillation and Risk of Ischemic Stroke) study with normal left ventricular ejection fraction (n=503; mean age 77 years, 63% women) underwent 14-day electrocardiographic monitoring and 2-dimensional echocardiography. Frequent PVCs were defined as PVCs >500 per 24 hours and NSVT as ≥4 consecutive ventricular ectopic beats. Reduced LV global longitudinal strain (GLS) was used as an indicator of subclinical LV dysfunction. Seventy-six participants (15.1%) had PVCs >500/d, 117 (23.3%) had NSVT episodes. LV GLS was significantly reduced in both frequent PVCs and NSVT groups (P<0.01). In multivariable analyses, lower LV GLS was associated with frequent PVCs (adjusted odds ratio [aOR], 1.19 [95% CI, 1.09-1.30 per unit reduction]; P<0.001) and NSVT (aOR, 1.09 [95% CI, 1.01-1.17]; P=0.036) independently of established risk factors and other echocardiographic parameters. Abnormal LV GLS (>-15.8%) carried a 2-fold increase in risk of ventricular arrhythmias (aOR, 2.18, P=0.029 for PVCs; aOR, 2.09, P=0.026 for NSVT). Conclusions PVCs and NSVT episodes were frequent in this community-based elderly cohort with normal left ventricular ejection fraction and were independently associated with lower LV GLS. The association between LV dysfunction and ventricular arrhythmias is present at an early, subclinical stage, an observation that carries possible preventative implications.

Racial, ethnic, and sex disparities in atrial fibrillation management: rate and rhythm control

BACKGROUND

Atrial fibrillation (AF) affects around 6 million Americans. AF management involves pharmacologic therapy and/or interventional procedures to control rate and rhythm, as well as anticoagulation for stroke prevention. Different populations may respond differently to distinct management strategies. This review will describe disparities in rate and rhythm control and their impact on outcomes among women and historically underrepresented racial and/or ethnic groups.

METHODS

This is a narrative review exploring the topic of sex and racial and/or ethnic disparities in rate and rhythm management of AF. We describe basic terminology, summarize AF epidemiology, discuss diversity in clinical research, and review landmark clinical trials.

RESULTS

Despite having higher rates of traditional AF risk factors, Black and Hispanic adults have lower risk of AF than non-Hispanic White (NHW) patients, although those with AF experience more severe symptoms and report lower quality-of-life scores than NHW patients with AF. NHW patients receive antiarrhythmic drugs, cardioversions, and invasive therapies more frequently than Black and Hispanic patients. Women have lower rates of AF than men, but experience more severe symptoms, heart failure, stroke, and death after AF diagnosis. Women and people from diverse racial and ethnic backgrounds are inadequately represented in AF trials; prevalence findings may be a result of underdetection.

CONCLUSION

Race, ethnicity, and gender are social determinants of health that may impact the prevalence, evolution, and management of AF. This impact reflects differences in biology as well as disparities in treatment and representation in clinical trials.

Unique Cardiovascular Disease Risk Factors in Hispanic Individuals

Purpose of Review

This review summarizes contemporary data on unique cardiovascular disease (CVD) risk factors in Hispanic individuals in the USA, and how addressing these factors is important in addressing health equity.

Recent Findings

Recent studies have shown high rates of traditional CVD risk factors in Hispanic individuals such as obesity, hypertension, diabetes, hyperlipidemia, and emerging CVD risk factors like hypertensive disorders of pregnancy, psychological stress, and occupational exposures. However, most studies fail to consider the significant heterogeneity in risk factor burden and outcomes in atherosclerotic CVD by Hispanic subgroup. Heart failure and rhythm disorders are less well studied in Hispanic adults, making risk assessment for these conditions difficult. High levels of CVD risk factors in Hispanic youth given an aging Hispanic population overall highlight the importance of risk mitigation among these individuals.

Summary

In brief, these data highlight the significant, unique burden of CVD risk among Hispanic individuals in the USA and predict a rising burden of disease among this growing and aging population. Future CVD research should focus on including robust, diverse Hispanic cohorts as well as specifically delineating results for disaggregated Hispanic groups across CVDs. This will allow for better risk assessment, prevention, and treatment decisions to promote health equity for Hispanic patients.

Sympathovagal Balance Is a Strong Predictor of Post High-Volume Endurance Exercise Cardiac Arrhythmia

Regular physical activity is important for cardiovascular health. However, high-volume endurance exercise has been associated with increased number of electrocardiogram (ECG) abnormalities, including disturbances in cardiac rhythm (arrhythmias) and abnormalities in ECG pattern. The aim of this study was to assess if heart rate variability (HRV) is associated with ECG abnormalities. Fifteen participants with previous cycling experience completed a 21-day high-volume endurance exercise cycle over 3,515 km. Participants wore a 5-lead Holter monitor for 24 h pre- and post-exercise, which was used to quantify ECG abnormalities and export sinus R-to-R intervals (NN) used to calculate HRV characteristics. As noise is prevalent in 24-h HRV recordings, both 24-h and heart rate collected during stable periods of time (i.e., deep sleep) were examined. Participants experienced significantly more arrhythmias post high-volume endurance exercise (median = 35) compared to pre (median = 12; p = 0.041). All 24-h and deep sleep HRV outcomes were not different pre-to-post high-volume endurance exercise (p &gt; 0.05). Strong and significant associations with arrhythmia number post-exercise were found for total arrhythmia (total arrhythmia number pre-exercise, ρ = 0.79; age, ρ = 0.73), supraventricular arrhythmia (supraventricular arrhythmia number pre-exercise: ρ = 0.74; age: ρ = 0.66), and ventricular arrhythmia (age: ρ = 0.54). As a result, age and arrhythmia number pre-exercise were controlled for in hierarchical regression, which revealed that only deep sleep derived low frequency to high frequency (LF/HF) ratio post high-volume endurance exercise predicted post total arrhythmia number (B = 0.63, R2Δ = 34%, p = 0.013) and supraventricular arrhythmia number (B = 0.77, R2Δ = 69%, p &lt; 0.001). In this study of recreationally active people, only deep sleep derived LF/HF ratio was associated with more total and supraventricular arrhythmias after high-volume endurance exercise. This finding suggests that measurement of sympathovagal balance during deep sleep might be useful to monitor arrhythmia risk after prolonged high-volume endurance exercise performance.