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大规模评估智能手表以识别心房颤动。

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

机构信息

From the Division of Cardiovascular Medicine (M.V.P.), Stanford Center for Clinical Research (K.W.M., A.R., N.T.), the Quantitative Sciences Unit (H.H., A.G., V.B., J.L., S.E.G., M.D.), Information Resources and Technology (T.F., G.H.), Department of Medicine (S.D.), and the Center for Digital Health (M.P.T.), Stanford University, Stanford, Apple, Cupertino (L.C., D.N., A.B., S.D.), and the Veterans Affairs Palo Alto Health Care System, Palo Alto (M.P.T.) - all in California; the University of Colorado School of Medicine, Aurora (J.S.R.); the Division of Cardiovascular Disease, Cooper Medical School of Rowan University, Camden, NJ (A.M.R.); the Lankenau Heart Institute and Jefferson Medical College, Philadelphia (P.K.); StopAfib.org, American Foundation for Women's Health, Decatur, TX (M.T.H.); and the Duke Clinical Research Institute, Duke University, Durham, NC (C.B.G.).

出版信息

N Engl J Med. 2019 Nov 14;381(20):1909-1917. doi: 10.1056/NEJMoa1901183.

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.).

摘要

背景

可穿戴设备上的光学传感器可以检测不规则脉冲。目前尚不清楚智能手表应用程序(app)在常规使用中识别房颤的能力。

方法

无房颤的参与者(由参与者自身报告)使用智能手机(Apple iPhone)应用程序同意进行监测。如果基于智能手表的不规则脉冲通知算法识别出可能的房颤,则启动远程医疗就诊,并向参与者邮寄心电图(ECG)贴片,佩戴长达 7 天。在通知不规则脉冲后 90 天和研究结束时进行调查。主要目的是估计在 ECG 贴片上显示的通知参与者中房颤的比例,以及具有目标置信区间宽度为 0.10 的不规则脉冲间隔的阳性预测值。

结果

我们在 8 个月内招募了 419297 名参与者。在中位数为 117 天的监测期间,2161 名参与者(0.52%)收到不规则脉冲通知。在 450 名返回含有可分析数据的 ECG 贴片的参与者中 - 平均在通知后 13 天应用 - 房颤的总体发生率为 34%(97.5%置信区间[CI],29 至 39),在 65 岁或以上的参与者中为 35%(97.5%CI,27 至 43)。在收到不规则脉冲通知的参与者中,在随后的不规则脉冲通知同时观察到心电图上的房颤的阳性预测值为 0.84(95%CI,0.76 至 0.92),在随后的不规则心动过速计同时观察到心电图上的房颤的阳性预测值为 0.71(97.5%CI,0.69 至 0.74)。在 1376 名收到 90 天调查回复的通知参与者中,57%的参与者联系了研究以外的医疗保健提供者。没有报告与应用程序相关的严重不良事件。

结论

收到不规则脉冲通知的概率很低。在收到不规则脉冲通知的参与者中,34%的人在随后的 ECG 贴片读数中有房颤,84%的通知与房颤一致。这种无现场(参与者无需现场就诊)的实用研究设计为使用用户自有设备可靠评估结局或依从性的大规模实用研究奠定了基础。(由苹果公司资助;Apple Heart Study ClinicalTrials.gov 编号,NCT03335800)。

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