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苹果设备的被动心房颤动检测能否预防中风?利用真实世界的用户数据估算高危可干预患者的比例。

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data.

机构信息

Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, Missouri, USA.

Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, USA.

出版信息

J Am Med Inform Assoc. 2022 May 11;29(6):1040-1049. doi: 10.1093/jamia/ocac009.

Abstract

OBJECTIVE

Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data.

MATERIALS AND METHODS

This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model.

RESULTS

Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis.

DISCUSSION AND CONCLUSION

These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

摘要

目的

利用整合后的电子健康记录(EHR)和消费者级可穿戴设备数据,我们旨在提供基于可穿戴设备数据诊断心房颤动(AFib)时佩戴者可能受益的比例的真实世界估计。

材料和方法

本研究利用了 2015 年 4 月 25 日至 2018 年 11 月 16 日期间,在 Cedars-Sinai 医疗中心的 1802 名患者的 EHR 和 Apple Watch 数据进行观察性队列研究,这些患者将设备与 EHR 关联。使用这些数据,我们根据(1)病史、(2)Apple Watch 佩戴模式和(3)现有经过验证的模型确定的 AFib 风险,估算了可通过抗凝治疗的高危患者数量。

结果

根据该队列的特征,根据 Apple Watch 确定的 AFib,平均有 0.25%(n=4.58,95%CI,2.0-8.0)的患者可能适合新的抗凝治疗。仅使用 EHR 数据,我们发现,即使在新诊断出 AFib 后,在 1802 名患者中,也只有约 36%(n=665.93,95%CI,626.0-706.0)的患者会被推荐使用抗凝治疗。

讨论和结论

这些数据表明,在该队列中,通过检测和抗凝治疗 AFib 的获益有限,但从 EHR 中获取临床和人口统计学数据可以帮助将设备针对最有可能受益的患者。未来的研究可能会在其他地点和其他可穿戴设备使用者中分析这种关系,包括那些尚未将设备与 EHR 关联的患者。

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