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Updated: Jun 30, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Discrimination of atrial fibrillation burden using cardiac magnetic resonance imaging
Andreas U Gasser1,2, Stefanie Aeschbacher1,2, Michael Coslovsky1,3
1Department of Cardiology, University Hospital Basel, Basel, Switzerland.
Insights
Cardiac imaging significantly improves stratification of atrial fibrillation (AF) burden compared to clinical factors alone. This finding highlights the potential of cardiac imaging as a tool for estimating AF burden and its prognostic implications.
Area of Science:
- Cardiology
- Medical Imaging
- Electrophysiology
Background:
- Atrial fibrillation (AF) burden is increasingly recognized for its prognostic significance.
- Accurate stratification of AF burden is crucial for patient management and risk assessment.
Purpose of the Study:
- To evaluate the effectiveness of clinical and cardiac imaging variables in differentiating between high and low AF burden.
- To compare the discriminative power of clinical data versus cardiac imaging for AF burden stratification.
Main Methods:
- Analysis of data from the prospective, multicenter Swiss-AF Burden study.
- Utilized 7-day Holter electrocardiogram and cardiac magnetic resonance imaging for AF burden assessment.
- Employed logistic regression models and Area Under the Curve (AUC) to evaluate discriminative performance.
Main Results:
- Cardiac imaging variables demonstrated superior performance in stratifying AF burden compared to clinical variables.
- The imaging model achieved an AUC of 0.91 (0.84-0.98), significantly outperforming the clinical model (AUC 0.67; 0.58-0.77).
- Combining clinical and imaging models yielded a marginal improvement (AUC 0.92; 0.86-0.99) over the imaging model alone.
Conclusions:
- Cardiac imaging variables are highly effective in stratifying patients into high and low AF burden categories.
- Cardiac imaging shows significant potential as a non-invasive tool for estimating AF burden.
- These findings support the integration of cardiac imaging into clinical practice for AF management.
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.
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