Atrial Fibrillation (AF)
Conditions
Keywords
Atrial Fibrillation, Athletes, Sport, Physical Activity, Exercise, Arrhythmias
Brief summary
Exercise is beneficial to heart health, however, there appears to be a 'U' shaped relationship where too much exercise may increase the risk of an irregular heart rhythm, called atrial fibrillation. Endurance athletes may have up to a 2.5-fold higher risk of developing atrial fibrillation than non-athletic controls. The mechanisms behind this increased risk of atrial fibrillation are not the well understood. It is thought to be a mixture of enlarged heart chambers, low resting heart rate, genetic predisposition and possibly scarring in the heart. In this study, the investigators will investigate the electrical activity changes in the heart, using a high-quality electrocardiogram (ECG) and relate this to changes in the heart size measured by ultrasound and MRI. Cardiopulmonary exercise testing will determine fitness (V̇O2 max) and assess the heart's electrical activity during exercise. This will be a case-control study where athletes with and without atrial fibrillation will be recruited. The investigators hope the results of this study can improve our understanding of atrial fibrillation in athletes by associating atrial fibrillation with structural and electrical differences which may aid the prediction of future atrial fibrillation development and help guide more athlete-specific treatment pathways.
Interventions
None listed
Sponsors
University of Leicester
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* ≥18 years of age at the time of enrolment, male and female. * History of atrial fibrillation confirmed on ECG - either paroxysmal or persistent. * Competitive athlete. Defined as: 1. Competed in endurance sports with a total cumulative moderate to high intensity of \> 1500 hours. 2. Have participated in at least one competitive event in the last 10 years.
Exclusion criteria
* Permanent atrial fibrillation. * History of pre-existing cardiovascular disease : 1. Atherosclerotic disease: previous myocardial infarction, symptomatic coronary artery disease or Peripheral peripheral arterial disease 2. Left ventricular systolic dysfunction (EF \< 45%) 3. Heart muscle disease: cardiomyopathies, Infiltrative diseases of the heart 4. Complex Congenital heart disease 5. Moderate or severe valvular disease 6. Uncontrolled hypertension (\>180/100mmHg)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| High-resolution ECG | At study visit | Using high quality ECG, assess whether subtle differences can be detected in athletes with AF, compared to athletes without AF, and whether machine learning could predict new-onset AF. Detection of subtle differences in p wave parameters (duration, amplitude, dispersion, PTFV1) in athletes with AF compared to athletes without AF. AUC, specificity and sensitivity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 72hr heart rate monitoring | At study visit | Compare autonomic tone via heart rate variability from 72-hour continuous ECG monitoring in athletes with and without AF. Analysis of RR intervals from heart rate variability. |
| Electronic stethoscope recording | At study visit | Compare the heart sounds using electronic stethoscope in athletes with and without AF. S1 and S2 sounds of heart valves. |
| AI classification and prediction | At study visit | Assess the accuracy of using machine learning to identify athletes with AF using ECG data. AUC, specificity and sensitivity of machine learning identification of AF. |
| Cardiopulmonary exercise testing | At study visit | Peak VO2 |
| Cardiac motion recording | At study visit | Cardiac angular velocity |
| Cardiac imaging | At study visit | Left ventricular mass |
Countries
United Kingdom
Contacts
Primary ContactCai L Davies
Outcome results
None listed