Department of Cardiac Function, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Rd, Xuhui District, 200030, Shanghai, China.
BMC Cardiovasc Disord. 2021 Nov 20;21(1):558. doi: 10.1186/s12872-021-02363-1.
Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm.
This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s.
Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76-97.80%), 88.68% (95% CI 77.06-95.07%), and 100% (95% CI 92.91-100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21-99.44%), 94.34% (95% CI 84.03-98.65%), and 100% (95% CI 92.91-100%), respectively. Similar results as those of the upright position were obtained after exercise.
The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.
心房颤动（AF）是最常见的心律失常，发病率和死亡率都很高。有证据表明，每 3 个 AF 患者中就有 1 个是无症状的。AF 的无症状和阵发性特征是传统仪器检测不理想和延迟的原因。研究表明，佩戴动态心电图（ECG）记录仪可以指导 AF 与正常窦性节律的准确、安全分析、解释和区分。人工智能（AI）算法也可以在直立位和运动后 60 秒内实现这一点。
本研究于 2020 年 6 月 24 日至 7 月 24 日从我院门诊登记处招募了 114 名参与者。参与者使用可穿戴动态心电图记录仪和 12 导联心电图在仰卧位、直立位和运动后 60 秒进行测试。
在纳入研究的 114 名受试者中，61 名受试者窦性心律正常，53 名受试者心房颤动。无法通过动态心电图记录仪腕带确定的病例数分别为 2、1 和 1。诊断准确性、敏感性和特异性测试结果为仰卧位时，可穿戴动态心电图记录仪的结果分别为 94.74%（95%CI%88.76-97.80%）、88.68%（95%CI 77.06-95.07%）和 100%（95%CI 92.91-100%）。同时，直立位时的诊断准确性、敏感性和特异性分别为 97.37%（95%CI 92.21-99.44%）、94.34%（95%CI 84.03-98.65%）和 100%（95%CI 92.91-100%）。运动后也得到了类似于直立位的结果。
集成 AI 算法的普及型可穿戴动态心电图记录仪可以有效地在不同体位和运动后检测 AF。因此，作为一种及时诊断高危人群 AF 的有用且用户友好的筛查方法，该工具具有很大的应用前景。
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