Korean Circ J.  2023 Oct;53(10):677-689. 10.4070/kcj.2023.0012.

Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation

• Kwon S
• Lee E
• Ju H
• Ahn HJ
• Lee SR
• Choi EK
• Suh J
• Oh S
• Rhee W

• KMID: 2546991
• DOI: http://doi.org/10.4070/kcj.2023.0012

Abstract

Background and Objectives
There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients.
Methods
We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features.
Results
Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model’s performance.
Conclusions
Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

Keyword

Atrial fibrillation; Electric countershock; Machine learning; Recurrence

Figure

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  Figure 1 The enrollment and study flow.AF = atrial fibrillation; ECV = electrical cardioversion.

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  Figure 2 The flow of data construction for machine learning.Eight clinical features and 42 ECG features were used for machine learning.ECG = electrocardiogram.

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  Figure 3 Predictive performance of the machine learning model for 1-month AF recurrence after electrical cardioversion according to trained datasets.The XGBoost model training both ECGs and clinical features improved the predictive performance compared with training either ECGs or clinical features alone. The dotted line represents the best AUROC based on the C-statistics of the AF durations.AF = atrial fibrillation; AUROC = area under the receiver operating characteristic curve; ECG = electrocardiogram; XGBoost = extreme gradient boost.

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  Figure 4 The visualization of the feature of importance for the machine learning model.The AF duration was the most important feature among the clinical parameters. Among all the features, AF ECG features were of high priority for the machine learning model to predict 1-month recurrence after ECV. The x-axis represents the frequency of a feature being utilized to split the data across all trees in the machine learning model. A higher value on the x-axis indicates greater importance of the feature in classifying patients with and without 1-month recurrence of AF.AAD = antiarrhythmic drug; AF = atrial fibrillation; ECG = electrocardiogram; ECV = electrical cardioversion; LA = left atrial; LVEF = left ventricular ejection fraction.

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