Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.

Autor: Ruzieh M; Department 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., Bai C; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA., Meisel E; College of Medicine, University of Florida, Gainesville, FL, USA., Kramer EF; College of Medicine, University of Florida, Gainesville, FL, USA., Frechette RR; College of Medicine, University of Florida, Gainesville, FL, USA., Nassereddin AT; Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA., Smoot M; Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA., Edwards ES; Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA., Kurup V; Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA., Naccarelli GV; Penn State Heart and Vascular Institute, Penn State University, Hershey, PA, USA., Naik D; Department of Medicine, Division of Cardiovascular Medicine, College of Medicine, University of Florida, 1600 SW Archer road, PO Box100288, Gainesville, FL, 32610, USA., Kimmel SE; Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA., Mardini MT; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
Jazyk: angličtina
Zdroj: Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing [J Interv Card Electrophysiol] 2024 Jul 12. Date of Electronic Publication: 2024 Jul 12.
DOI: 10.1007/s10840-024-01868-x
Abstrakt: 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.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
Externí odkaz: