Abstract 4136721: Reducing the Use of Transesophageal Echocardiogram Prior to Cardioversion Using Machine Learning

Introduction/Background: Direct current cardioversion (DCC) is used to terminate atrial fibrillation and atrial flutter. Due to increased stroke risk post DCC, a transesophageal echocardiogram (TEE) is performed in selected patients to rule out left atrial appendage thrombus (LAAT) prior to DCC. The prevalence of LAAT is around 2.5% in the era of direct oral anticoagulants. This is a large proportion of patients with no LAAT on TEE and calls for strategies to improve patient selections for TEE before DCC. Research Questions/Hypothesis: In this study we aimed to determine if the use of machine learning could accurately predict the presence of LAAT among patients with nonvalvular atrial fibrillation and atrial flutter. Methods/Approach: We collected demographic, comorbid, and transthoracic echocardiographic data on 795 patients who underwent TEE prior to DCC for nonvalvular atrial fibrillation and atrial flutter. We developed and validated an explainable machine learning model using the eXtreme Gradient Boosting algorithm with 5x5 nested cross-validation. The algorithm used 37 variables to predict the probability of LAAT. Results/Data: The prevalence of LAAT in our cohort was 11.3%. Our model achieved an area under the curve (AUC) of 0.79 in predicting LAAT, figure 1A. In comparison, CHA2DS2-VASc score performed poorly in our cohort with an AUC of 0.48, figure 1B. The ranking of variables most strongly predictive of LAAT is shown in figure 1C, with left ventricle ejection fraction as the most instrumental. Patients were categorized into 10 groups based on the percentile of their predicted probability of having a thrombus. A lower percentile (e.g., 10%) indicates a lower probability of having a thrombus. By utilizing a cutoff point of 0.16, which includes 10.0% of the patients, we can exclude the presence of thrombus with 100% certainty. Conclusion: Machine learning, compared to traditional risk scores, offers enhanced predictive model for identifying LAAT and provides better guidance for TEE and anticoagulation therapy before DCC.