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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Using Atrial Cardiomyopathy Metrics to Characterize Atrial Fibrillation in Endurance Athletes
Luke W Spencer1, Paolo D' Ambrosio2, Monique Ohanian3
1Heart, Exercise & Research Trials (HEART) Lab, St Vincent's Institute, Melbourne, Victoria, Australia; Department of Medicine, University of Melbourne, Parkville, Victoria, Australia.
Background:
Atrial fibrillation (AF) is more prevalent in endurance athletes (EAs) than in the general population, but identifying athletes at greatest risk remains challenging.
Objectives:
This study sought to develop an AF risk assessment tool from associations between AF and electrophysiologic, imaging, and genetic markers of atrial cardiomyopathy in EAs.
Methods:
EAs and nonathletic control subjects (NAs) underwent comprehensive phenotyping with the use of cardiopulmonary exercise testing, echocardiography, 12-lead electrocardiography (ECG), Holter monitoring for premature atrial contractions (PACs), and biochemical analysis. A genotyping array was used to determine polygenic risk scores for AF (AF-PRS). Logistic regression was used to develop an AF probability model.
Results:
EAs (n = 353; median age 41 years, 74% male) had 40% larger LA volumes (42.9 vs 29.7 mL/m2; P < 0.001) than NAs (n = 97; median age 34 years, 64% male). Of the EAs, 96 (27%) had prevalent AF and demonstrated larger LA volumes (46.2 mL/m2 vs 41.9 mL/m2; P < 0.001), reduced LA strain (reservoir 26% vs 32%; P < 0.001), prolonged P-wave duration (124 vs 111 ms; P < 0.001), abnormal P-wave terminal force velocity in lead V1 (26% vs 9%; P < 0.001), and increased PACs (118 vs 15 PACs/24 h; P < 0.001) than EAs without AF. A clinical risk model incorporating these factors demonstrated excellent discrimination (AUC = 0.84). High AF-PRS increased AF odds 2.56-fold but contributed little additional predictive value. Using the model, AF prevalence increased from 4% to 86% in low to very high risk athletes, respectively.
Conclusions:
A combination of atrial cardiomyopathy markers was associated with high AF prevalence in EAs. A resulting quantitative AF risk identification tool provides promise for interventions aimed at preventing AF and thromboembolic complications in EAs.
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