Evidence map›Paper›PMID 35190832›Full record

Observational studyJournal of the American Medical Informatics Association : JAMIA2022

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

Keith Feldman, Ray G Duncan, An Nguyen, Galen Cook-Wiens, Yaron Elad, Teryl Nuckols, Joshua M Pevnick

Open access · bronzeAbstract readObservational Study
In one paragraph

Observational study in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Mayo Clinic proceedings. Digital health · 2023
    Review
  11. Centering the patient in informatics applications.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Keith FeldmanDivision of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, Missouri, USA.ORCID 0000-0001-6935-5844
Ray G DuncanEnterprise Information Services, Cedars-Sinai Health System, Los Angeles, California, USA.
An NguyenDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Galen Cook-WiensDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Yaron EladEnterprise Information Services, Cedars-Sinai Health System, Los Angeles, California, USA.
Teryl NuckolsDepartment of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Joshua M PevnickEnterprise Information Services, Cedars-Sinai Health System, Los Angeles, California, USA.
Cedars-Sinai Medical Center · USChildren's Mercy Hospital · US

Funding

Cedars-Sinai Precision Health
6 · The paper itself

Abstract

objectiveUtilizing 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

methodsThis 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.

resultsBased 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

conclusionThese 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.

Indexed as

Atrial FibrillationStrokeWearable Electronic DevicesAnticoagulantsHumansAnticoagulantsatrial fibrillationmobile health (mHealth)patient-generated dataprecision health

Identifiers

PMID35190832
PMCPMC9093037
OpenAlexW4210453385

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.