Atrial fibrillation in the athlete: Case report and a contemporary appraisal

Antiarrhythmic drugs for atrial fibrillation in the outpatient setting

With burgeoning evidence for early rhythm control in patients with atrial fibrillation (AF), the management of AF in the outpatient setting has become more challenging. The primary care clinician often serves as the frontline in the pharmacologic management of AF. Because of drug interactions and the risk of proarrhythmia, many clinicians remain hesitant about the initiation and chronic management of antiarrhythmic drug prescriptions. However, with the likely increased use of antiarrhythmics for early rhythm control, understanding and familiarity with these medications have likewise become more important, especially since patients with AF probably have other non-cardiac medical conditions that can impact their antiarrhythmic therapy. In this comprehensive review, we provide informative, high-yield cases and edifying references that will help primary care providers become comfortable handling various clinical scenarios.

An automated detection of atrial fibrillation from single‑lead ECG using HRV features and machine learning

BACKGROUND

Atrial fibrillation (AF) is a disorder of the heart rhythm where irregular and rapid heartbeats are observed. This supraventricular arrhythmia may increase the risk of blood clots, stroke, heart failure, and other serious heart complications. Automatic analysis of AF that is based on machine learning (ML) plays an important role in detecting this heart disease.

METHODS

A new approach for automated AF detection is presented using heart rate variability (HRV) features and machine learning. A set of time-domain, frequency-domain and nonlinear features are extracted from the R-R intervals. A new method for frequency-domain analysis of R-R intervals using the Fourier Decomposition Method is presented, which provides promising results as compared to the usual method of power spectral density estimation. We train the algorithm on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) atrial fibrillation database and perform a comprehensive analysis using statistical tests to obtain the results without any intra-patient bias.

RESULTS

The proposed method is able to achieve average result of 95.16% sensitivity, 92.46% specificity and 94.43% accuracy and its performance is better than the existing approaches. Furthermore, the efficacy of the proposed algorithm is tested on eight records from a previously unseen MIT-BIH Arrhythmia Database.

CONCLUSION

This work shows that the proposed HRV features and ML approach can be effectively used for the analysis, detection, and classification of AF.

Atrial fibrillation and sport: need for monitoring

Historically, regular exercise contributed to reduce the arrhythmic burden and improve cardiovascular outcomes in the general population. However, a heightened risk of Atrial Fibrillation (AF) seems to occur mainly amongst endurance athletes. The exact mechanisms are not fully elucidated, but dynamic interactions between electroanatomical changes induced by exercise, the autonomic system, variable triggers, along individual genetic predisposition are the main contributors to AF development in athletes. The type and training load of sports are also crucial in determining the arrhythmogenic milieu predisposing to AF insurgence and perpetuation. Moreover, a sex difference seems to influence an increased risk of AF only in men undergoing strenuous exercise, whereas women appear protected even during more vigorous training. In the absence of solid evidence, the advent of modern technologies could help to monitor and deep investigate the peculiar aspects of AF in these athletes. This review aims to describe the pathophysiology, diagnosis, and management of AF in athletes, shedding light on possible future strategies to face AF in this population.