Discrimination of atrial fibrillation burden using cardiac magnetic resonance imaging.
Description
Kühne and Zuern shared as last authors.
Publisher DOI
Abstract
Background
Recent studies indicate that atrial fibrillation (AF) burden has prognostic implications.
Objective
We aimed to assess the ability of clinical and cardiac imaging variables to stratify between high and low AF burden.
Method
Data from the prospective, multicenter Swiss-AF Burden study were analyzed. Patients underwent a 7-day Holter electrocardiogram and native cardiac magnetic resonance imaging. AF burden, defined as the percentage of time in AF during the 7-day Holter electrocardiogram, was dichotomized into low (<10%) or high (≥10%). Logistic regression models were built, and discriminative performance was evaluated by comparing the area under the curve (AUC).
Results
A total of 170 patients were enrolled (median age 72 years; 18% female); 26% (n = 44) had high AF burden. Variables selected for the clinical model were age (odds ratio 1.09; 95% confidence interval 0.40–2.74), male sex (1.05; 1.00–1.11), and body mass index (1.14; 1.06–1.24). The imaging model included left atrial maximal volume index (1.04; 1.01–1.06), left ventricular end-diastolic volume index (0.93; 0.90–0.96), right atrial fractional area change (0.94; 0.89–0.98), and left ventricular ejection fraction (0.87; 0.81–0.94). The AUCs for the clinical and imaging models were 0.67 (0.58–0.77) and 0.91 (0.84–0.98), respectively. Combining both models yielded an AUC of 0.92 (0.86–0.99), with no substantial improvement over the imaging model alone.
Conclusion
Cardiac imaging variables clearly outperformed clinical variables in their ability to stratify between high and low AF burden, suggesting their potential as a tool for estimating AF burden.
Recent studies indicate that atrial fibrillation (AF) burden has prognostic implications.
Objective
We aimed to assess the ability of clinical and cardiac imaging variables to stratify between high and low AF burden.
Method
Data from the prospective, multicenter Swiss-AF Burden study were analyzed. Patients underwent a 7-day Holter electrocardiogram and native cardiac magnetic resonance imaging. AF burden, defined as the percentage of time in AF during the 7-day Holter electrocardiogram, was dichotomized into low (<10%) or high (≥10%). Logistic regression models were built, and discriminative performance was evaluated by comparing the area under the curve (AUC).
Results
A total of 170 patients were enrolled (median age 72 years; 18% female); 26% (n = 44) had high AF burden. Variables selected for the clinical model were age (odds ratio 1.09; 95% confidence interval 0.40–2.74), male sex (1.05; 1.00–1.11), and body mass index (1.14; 1.06–1.24). The imaging model included left atrial maximal volume index (1.04; 1.01–1.06), left ventricular end-diastolic volume index (0.93; 0.90–0.96), right atrial fractional area change (0.94; 0.89–0.98), and left ventricular ejection fraction (0.87; 0.81–0.94). The AUCs for the clinical and imaging models were 0.67 (0.58–0.77) and 0.91 (0.84–0.98), respectively. Combining both models yielded an AUC of 0.92 (0.86–0.99), with no substantial improvement over the imaging model alone.
Conclusion
Cardiac imaging variables clearly outperformed clinical variables in their ability to stratify between high and low AF burden, suggesting their potential as a tool for estimating AF burden.
Date Issued
2026-06-19
Publication Type
Article
Language(s)
en
Author(s)
Gasser, Andreas U. | |
Aeschbacher, Stefanie | |
Coslovsky, Michael | |
Meier, Vincent | |
Ruoff, Tanja | |
Müller, Andreas S. | |
Bernheim, Alain M. | |
Beer, Jürg H. | |
Moschovitis, Giorgio | |
De Perna, Maria Luisa | |
Conen, David | |
Osswald, Stefan | |
Sticherling, Christian | |
Haaf, Philip | |
Krisai, Philipp | |
Kühne, Michael | |
Zuern, Christine S. |
Journal
Heart Rhythm O2
Publisher
Elsevier
ISSN
2666-5018
Access(Rights)
open.access