Department of Cardiology, University Hospital Basel, Basel, Switzerland; Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland.
Department of Cardiology, University Hospital Basel, Basel, Switzerland; Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland.
JACC Clin Electrophysiol. 2023 Feb;9(2):232-242. doi: 10.1016/j.jacep.2022.09.011. Epub 2023 Jan 18.
Multiple smart devices capable to detect atrial fibrillation (AF) are presently available. Sensitivity and specificity for the detection of AF may differ between available smart devices, and this has not yet been adequately investigated.
The aim was to assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead electrocardiogram as the reference standard in a real-world cohort of patients.
We consecutively enrolled patients presenting to a cardiology service at a tertiary referral center in a prospective, diagnostic study.
We prospectively analyzed 201 patients (31% women, median age 66.7 years). AF was present in 62 (31%) patients. Sensitivity and specificity for the detection of AF were comparable between devices: 85% and 75% for the Apple Watch 6, 85% and 75% for the Samsung Galaxy Watch 3, 58% and 75% for the Withings Scanwatch, 66% and 79% for the Fitbit Sense, and 79% and 69% for the AliveCor KardiaMobile, respectively. The rate of inconclusive tracings (the algorithm was unable to determine the heart rhythm) was 18%, 17%, 24%, 21%, and 26% for the Apple Watch 6, Samsung Galaxy Watch 3, Withings Scan Watch, Fitbit Sense, and AliveCor KardiaMobile (P < 0.01 for pairwise comparison), respectively. By manual review of inconclusive tracings, the rhythm could be determined in 955 (99%) of 969 single-lead electrocardiograms. Regarding patient acceptance, the Apple Watch was ranked first (39% of participants).
In this clinical validation of 5 direct-to-consumer smart devices, we found differences in the amount of inconclusive tracings diminishing sensitivity and specificity of the smart devices. In a clinical setting, manual review of tracings is required in about one-fourth of cases.
目前有多种能够检测心房颤动(AF)的智能设备。可用的智能设备在检测 AF 方面的敏感性和特异性可能有所不同,但这尚未得到充分研究。
本研究旨在评估 5 种智能设备在识别 AF 方面的准确性,以医生解读的 12 导联心电图作为参考标准,在真实患者队列中进行比较。
我们连续纳入了在一家三级转诊中心心内科就诊的前瞻性诊断研究患者。
我们前瞻性分析了 201 例患者(31%为女性,中位年龄 66.7 岁)。62 例(31%)患者存在 AF。AF 检测的敏感性和特异性在设备之间具有可比性:Apple Watch 6 为 85%和 75%,Samsung Galaxy Watch 3 为 85%和 75%,Withings Scanwatch 为 58%和 75%,Fitbit Sense 为 66%和 79%,AliveCor KardiaMobile 为 79%和 69%。不可靠心电图(算法无法确定心率)的比例分别为 Apple Watch 6、Samsung Galaxy Watch 3、Withings Scan Watch、Fitbit Sense 和 AliveCor KardiaMobile 的 18%、17%、24%、21%和 26%(两两比较 P<0.01)。通过不可靠心电图的手动复查,969 份单导联心电图中的 955 份(99%)可以确定节律。关于患者接受度,Apple Watch 排名第一(39%的参与者)。
在对 5 种直接面向消费者的智能设备进行临床验证中,我们发现不可靠心电图的数量存在差异,这降低了智能设备的敏感性和特异性。在临床环境中,大约四分之一的病例需要手动复查心电图。