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Updated: Jun 21, 2025
Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
Published on: February 26, 2013
Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion
Mohammed Ruzieh1, Chen Bai2, Emily Meisel3
1Department of Medicine, Division of Cardiovascular Medicine, College of Medicine, University of Florida, 1600 SW Archer road, PO Box100288, Gainesville, FL, 32610, USA. moh.ruzieh@gmail.com.
Insights
A new machine learning model improves prediction of left atrial appendage thrombus (LAAT) in patients with atrial fibrillation and atrial flutter. This tool offers better guidance for anticoagulation therapy and cardioversion decisions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Atrial fibrillation and atrial flutter are common cardiac arrhythmias.
- The CHA2DS2-VASc score has limitations in predicting stroke risk.
- Left atrial appendage thrombus (LAAT) is a significant stroke risk factor in these patients.
Purpose of the Study:
- To develop and validate an explainable machine learning model for improved prediction of LAAT.
- To enhance risk stratification for stroke in patients with atrial fibrillation and atrial flutter.
- To provide better guidance for anticoagulation therapy and cardioversion.
Main Methods:
- An explainable machine learning model using eXtreme Gradient Boosting was developed.
- The model was validated using 5x5 nested cross-validation.
- Input variables included 37 demographic, comorbid, and echocardiographic factors.
Main Results:
- The model achieved an AUC of 0.79, with 82% specificity and 57% sensitivity.
- Left ventricular ejection fraction was the most significant predictor of LAAT.
- A cutoff of 0.16 allowed 100% confidence in ruling out thrombus for 10% of patients.
Conclusions:
- Machine learning significantly refines LAAT prediction accuracy and model interpretability.
- The developed model shows promise for guiding anticoagulation and cardioversion strategies.
- This approach can lead to more personalized and effective patient management.
Background:
Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, its predictive power is limited. Therefore, we sought to improve risk prediction for left atrial appendage thrombus (LAAT), a known risk factor for stroke in these patients.
Methods:
We developed and validated an explainable machine learning model using the eXtreme Gradient Boosting algorithm with 5 × 5 nested cross-validation. The primary outcome was to predict the probability of LAAT in patients with atrial fibrillation and atrial flutter who underwent transesophageal echocardiogram prior to cardioversion. Our algorithm used 37 demographic, comorbid, and transthoracic echocardiographic variables.
Results:
A total of 795 patients were included in our analysis. LAAT was present in 11.3% of the patients. The average age of patients was 63.3 years and 34.7% were women. Patients with LAAT had significantly lower left ventricular ejection fraction (29.9% vs 43.5%; p < 0.001), lower E' lateral velocity (5.7 cm vs. 7.9 cm; p < 0.001) and higher E/A ratio (2.6 vs 1.8; p = 0.002). Our machine learning model achieved a high AUC of 0.79, with a high specificity of 0.82, and modest sensitivity of 0.57. Left ventricular ejection fraction was the most important variable in predicting LAAT. Patients were split into 10 buckets based on the percentile of their predicted probability of having thrombus. The lower the percentile (e.g., 10%), the lower the probability of having thrombus. Using a cutoff point of 0.16 which includes 10.0% of the patients, we can rule out thrombus with 100% confidence.
Conclusion:
Using machine learning, we refined the predictive power of predicting LAAT and explained the model. These results show promise in providing better guidance for anticoagulation therapy and cardioversion in AF and AFL patients.
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