Literature DB >> 35190832

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

Keith Feldman1,2, Ray G Duncan3,4, An Nguyen5, Galen Cook-Wiens5, Yaron Elad3,5, Teryl Nuckols6, Joshua M Pevnick3,6.   

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.
© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  atrial fibrillation; mobile health (mHealth); patient-generated data; precision health

Mesh:

Substances:

Year:  2022        PMID: 35190832      PMCID: PMC9093037          DOI: 10.1093/jamia/ocac009

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   7.942


  20 in total

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Authors:  Ying Xian; Emily C O'Brien; Li Liang; Haolin Xu; Lee H Schwamm; Gregg C Fonarow; Deepak L Bhatt; Eric E Smith; DaiWai M Olson; Lesley Maisch; Deidre Hannah; Brianna Lindholm; Barbara L Lytle; Michael J Pencina; Adrian F Hernandez; Eric D Peterson
Journal:  JAMA       Date:  2017-03-14       Impact factor: 56.272

3.  Disparities in enrollment and use of an electronic patient portal.

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4.  Variations in cause and management of atrial fibrillation in a prospective registry of 15,400 emergency department patients in 46 countries: the RE-LY Atrial Fibrillation Registry.

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Journal:  Circulation       Date:  2014-01-24       Impact factor: 29.690

5.  Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial.

Authors:  Steven R Steinhubl; Jill Waalen; Alison M Edwards; Lauren M Ariniello; Rajesh R Mehta; Gail S Ebner; Chureen Carter; Katie Baca-Motes; Elise Felicione; Troy Sarich; Eric J Topol
Journal:  JAMA       Date:  2018-07-10       Impact factor: 56.272

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

Authors:  Marco V Perez; Kenneth W Mahaffey; Haley Hedlin; John S Rumsfeld; Ariadna Garcia; Todd Ferris; Vidhya Balasubramanian; Andrea M Russo; Amol Rajmane; Lauren Cheung; Grace Hung; Justin Lee; Peter Kowey; Nisha Talati; Divya Nag; Santosh E Gummidipundi; Alexis Beatty; Mellanie True Hills; Sumbul Desai; Christopher B Granger; Manisha Desai; Mintu P Turakhia
Journal:  N Engl J Med       Date:  2019-11-14       Impact factor: 176.079

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Journal:  Br J Clin Pharmacol       Date:  2021-07-13       Impact factor: 4.335

9.  Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium.

Authors:  Alvaro Alonso; Bouwe P Krijthe; Thor Aspelund; Katherine A Stepas; Michael J Pencina; Carlee B Moser; Moritz F Sinner; Nona Sotoodehnia; João D Fontes; A Cecile J W Janssens; Richard A Kronmal; Jared W Magnani; Jacqueline C Witteman; Alanna M Chamberlain; Steven A Lubitz; Renate B Schnabel; Sunil K Agarwal; David D McManus; Patrick T Ellinor; Martin G Larson; Gregory L Burke; Lenore J Launer; Albert Hofman; Daniel Levy; John S Gottdiener; Stefan Kääb; David Couper; Tamara B Harris; Elsayed Z Soliman; Bruno H C Stricker; Vilmundur Gudnason; Susan R Heckbert; Emelia J Benjamin
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10.  Clinical evaluation and diagnostic yield following evaluation of abnormal pulse detected using Apple Watch.

Authors:  Kirk D Wyatt; Lisa R Poole; Aidan F Mullan; Stephen L Kopecky; Heather A Heaton
Journal:  J Am Med Inform Assoc       Date:  2020-07-01       Impact factor: 4.497

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