美国斯坦福临床研究中心Marco V. Perez和Mintu P. Turakhia小组近日取得一项新成果。他们的大规模研究显示，智能手表可有效识别心房颤动。这一研究成果2019年11月14日发表在国际顶尖学术期刊《新英格兰医学杂志》上。

可穿戴设备上的光学传感器能够检测人体的不规则脉冲。智能手表应用程序（app）在佩戴过程中识别心房颤动的功能尚未明确。

研究组在8个月内招募了419297名参与者。这些参与者均没有心房颤动，使用苹果手机app进行监测。如果智能手表基于不规则脉冲算法识别出可能的心房颤动，则启动远程医疗就诊，研究组将心电图（ECG）贴片邮寄给受试者，最多可佩戴7天。

在117天的监测中，2161名参与者（0.52%）收到不规则脉冲预警，其中450名寄回了ECG贴片，这些贴片均含有可分析的数据。智能手表预警后平均13天，这450例患者心房颤动的发生率为34%，65岁及以上参与者的发生率为35%。

不规则脉冲预警对ECG观察到的心房颤动的阳性预测值为0.84，而不规则心动图的阳性预测值为0.71。1376名参与者接受了90天随访，57%的人接受了额外的医疗救助。所有患者均未发生与app相关的严重不良事件。

总之，参与者接收到不规则脉冲预警的概率很低。在收到预警的参与者中，34%在随后的ECG贴片中检测出房颤，84%的预警与ECG同时观察到的房颤一致。该结果表明佩戴智能手表可有效识别心房颤动。

附：英文原文

Title: Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Author: Marco V. Perez, M.D.,, Kenneth W. Mahaffey, M.D.,, Haley Hedlin, Ph.D.,, John S. Rumsfeld, M.D., Ph.D.,, Ariadna Garcia, M.S.,, Todd Ferris, M.D.,, Vidhya Balasubramanian, M.S.,, Andrea M. Russo, M.D.,, Amol Rajmane, M.D.,, Lauren Cheung, M.D.,, Grace Hung, M.S.,, Justin Lee, M.P.H.,, Peter Kowey, M.D.,, Nisha Talati, M.B.A.,, Divya Nag,, Santosh E. Gummidipundi, M.S.,, Alexis Beatty, M.D., M.A.S.,, Mellanie True Hills, B.S.,, Sumbul Desai, M.D.,, Christopher B. Granger, M.D.,, Manisha Desai, Ph.D.,, and Mintu P. Turakhia, M.D., M.A.S.

Issue&Volume: 2019-11-13

Abstract:

Background

Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.

Methods

Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.

Results

We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed — which had been applied, on average, 13 days after notification — atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.

Conclusions

The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.)

DOI: 10.1056/NEJMoa1901183

Source: https://www.nejm.org/doi/full/10.1056/NEJMoa1901183