基于人工智能心电图差异预测冷冻消融术后心房颤动复发

Prediction of atrial fibrillation recurrence based on artificial intelligence electrocardiogram difference

目的开发1项基于12导联心电图的机器学习模型预测冷冻球囊消融术后心房颤动(房颤)复发。方法本研究为前瞻性、单中心队列研究。纳入2020年5月至2023年9月就诊于天津医科大学第二医院并接受冷冻球囊消融术的房颤患者。收集患者临床资料以及术前和术后24 h内的标准12导联心电图。利用XGBoost方法构建3种基于术前和术后心电图数据差异的人工智能心电图模型。这些模型考虑了心电图参数、心电图深度特征、早期复发和基线房颤类型等55个特征的不同组合,并对其在模型预测中的重要性进行排序。结果共入选患者201例,年龄(68.0±9.3)岁,其中男91例(45.3%,91/201)。随访222(124,368)d,26例(12.9%,26/201)患者复发。最佳机器学习模型是以术前、术后心电图深度特征差值作为输入的模型,受试者工作特征曲线的曲线下面积(AUC)为0.872,F1得分为0.600,敏感度(召回率)为60.0%,特异度为94.4%,准确度为90.2%。结论人工智能辅助分析心电图能够预测冷冻球囊消融术后房颤复发。

Objective To develop a machine learning model based on 12-lead electrocardiogram(ECG)to predict recurrence of atrial fibrillation(AF)after cryoballoon ablation.Methods It was a prospective,single-center cohort study.Patients with AF who were admitted to The Second Hospital of Tianjin Medical University from May 2020 to September 2023 and underwent cryoballoon ablation were enrolled.Clinical data and standard 12-lead ECG within 24 hours before and after ablation were collected.Using the XGBoost method,three types of artificial intelligence(AI)ECG models were constructed based on the differences in ECG data before and after ablation.These models took into account considered different combinations of 55 features,including ECG parameters,ECG deep features,early recurrence and baseline AF types,and ranked their importance in model predictions.Results A total of 201 patients were included,with an average age of(68.0±9.3)years and 91(45.3%,91/201)males.After 222(124,368)days of follow-up,there were 26(12.9%,26/201)patients with recurrence.The best prediction performance was obtained from the model using ECG deep features as input,with area under curve(AUC)of 0.872,F1 score of 0.600,sensitivity(recall)of 60.0%,specificity of 94.4%and accuracy of 90.2%.Conclusion AI algorithms can predict recurrence of AF after cryoballoon ablation.