Zhang Yuji, Xu Shusheng, Xing Wenhui, Chen Qiong, Liu Xu, Pu Yachuan, Xin Fangran, Jiang Hui, Yin Zongtao, Tao Dengshun, Zhou Dong, Zhu Yan, Yuan Binhang, Jin Yan, He Yuanchen, Wu Yi, Po Sunny S, Wang Huishan, Benditt David G
Department of Cardiovascular Surgery General Hospital of Northern Theater Command Shenyang Liaoning China.
Institute for Interdisciplinary Information Sciences, Tsinghua University Beijing China.
J Am Heart Assoc. 2024 Feb 6;13(3):e032100. doi: 10.1161/JAHA.123.032100. Epub 2024 Jan 23.
Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high-risk patients is important but labor-intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges.
We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI-based tool for subsequent AF detection using ECG records alone. A total of 5 million 30-second epochs from 329 patients were annotated as AF or non-AF by expert ECG readers for AI training and validation, while 5 million 30-second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively.
Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.
心房颤动（AF）会增加栓塞性中风的风险，在术后患者中，还会增加护理成本。因此，对高危患者进行心电图筛查AF很重要，但劳动强度大。人工智能（AI）可能会减少AF检测工作量，但AI开发存在挑战。
我们采用了一种新颖的AI开发方法来检测AF，使用术后心脏患者的体表心电图记录和心房心外膜电图。心房电图仅用于促进为AI开发建立真正的AF；这允许建立一个仅使用心电图记录进行后续AF检测的基于AI的工具。329名患者的总共500万个30秒时段由专业心电图阅读者标注为AF或非AF，用于AI训练和验证，而来自330名不同患者的500万个30秒时段用于AI测试。在时段水平以及患者水平的AF负荷方面评估AI性能。AI在验证时的受试者操作特征曲线下面积为0.932，在测试时为0.953。在时段水平，测试结果显示AF检测的敏感性、特异性、阴性预测值、阳性预测值和F1（阳性预测值和敏感性的调和平均值）的均值分别为0.970、0.814、0.976、0.776和0.862，而AF负荷检测的组内相关系数为0.952。在患者水平，AF负荷敏感性和阳性预测率分别为96.2%和94.5%。
心房电图和体表心电图的使用允许开发一种强大的基于AI的方法来识别术后AF和评估AF负荷。这种新颖的工具可能会增强AF的检测和管理，特别是在心脏手术后的患者中。
