PDF(Pubmed)
Abstract：
BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence.
METHODS: Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score.
RESULTS: A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%.
CONCLUSIONS: The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models\' performance and may help identify patients at low risk for AF recurrence after index ablation.
METHODS: Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score.
RESULTS: A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%.
CONCLUSIONS: The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models\' performance and may help identify patients at low risk for AF recurrence after index ablation.
摘要：
背景：尽管房颤（AF）的治疗策略有所改善，相当比例的患者在消融术后仍有复发.本研究旨在提出一种基于Transformer的新算法，该算法利用体表心电图（ECG）信号和临床特征来预测房颤复发。
方法：在2018年10月至2021年12月之间，纳入了在窦性心律期间接受了至少一个标准10秒表面心电图的房颤射频消融术的患者。基于Transformer和融合模块的端到端深度学习框架用于使用ECG和临床特征预测AF复发。使用接收器工作特征曲线下面积（AUROC）评估模型性能，灵敏度,特异性，准确性和F1分数。
结果：总共920名患者（中位年龄61[IQR14]岁，66.3％的男性）被包括在内。经过24个月的中位随访，253例患者（27.5%）出现房颤复发。单个深度学习启用的ECG信号识别出AF复发，AUROC为0.769，灵敏度为75.5％，特异性为61.1%，F1评分为55.6%，总体准确率为65.2%。结合心电信号和临床特征，AUROC提高到0.899，灵敏度提高到81.1%，特异性为81.7%，F1得分达到71.7%，总体准确率为81.5%。
结论：Transformer算法在预测房颤复发方面表现出优异的性能。结合心电图和临床特征可增强模型的性能，并有助于识别指征消融术后房颤复发风险较低的患者。
方法：在2018年10月至2021年12月之间，纳入了在窦性心律期间接受了至少一个标准10秒表面心电图的房颤射频消融术的患者。基于Transformer和融合模块的端到端深度学习框架用于使用ECG和临床特征预测AF复发。使用接收器工作特征曲线下面积（AUROC）评估模型性能，灵敏度,特异性，准确性和F1分数。
结果：总共920名患者（中位年龄61[IQR14]岁，66.3％的男性）被包括在内。经过24个月的中位随访，253例患者（27.5%）出现房颤复发。单个深度学习启用的ECG信号识别出AF复发，AUROC为0.769，灵敏度为75.5％，特异性为61.1%，F1评分为55.6%，总体准确率为65.2%。结合心电信号和临床特征，AUROC提高到0.899，灵敏度提高到81.1%，特异性为81.7%，F1得分达到71.7%，总体准确率为81.5%。
结论：Transformer算法在预测房颤复发方面表现出优异的性能。结合心电图和临床特征可增强模型的性能，并有助于识别指征消融术后房颤复发风险较低的患者。
