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American College of Cardiology

Smartwatch Algorithm for Automated Detection of Atrial Fibrillation

Overview of attention for article published in JACC, March 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
2 news outlets
blogs
3 blogs
policy
1 policy source
twitter
295 X users
patent
1 patent
facebook
5 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
362 Dimensions

Readers on

mendeley
405 Mendeley
Title
Smartwatch Algorithm for Automated Detection of Atrial Fibrillation
Published in
JACC, March 2018
DOI 10.1016/j.jacc.2018.03.003
Pubmed ID
Authors

Joseph M Bumgarner, Cameron T Lambert, Ayman A Hussein, Daniel J Cantillon, Bryan Baranowski, Kathy Wolski, Bruce D Lindsay, Oussama M Wazni, Khaldoun G Tarakji

Abstract

The Kardia Band (KB) is a novel technology that enables patients to record a rhythm strip using an Apple smartwatch. The band is paired with an app providing automated detection of atrial fibrillation (AF). To examine whether the KB could accurately differentiate sinus rhythm (SR) from AF compared to physician-interpreted 12-lead ECGs and KB recordings. Consecutive patients with AF presenting for cardioversion (CV) were enrolled. Patients underwent pre-CV ECG along with a KB recording. If CV performed, a post-CV ECG was obtained along with a KB recording. The KB interpretations were compared to physician-reviewed ECGs. The KB recordings were reviewed by blinded electrophysiologists and compared to ECG interpretations. Sensitivity, specificity and K coefficient were measured. One hundred patients were enrolled (Age 68 ± 11 years). Eight patients did not undergo CV. There were 169 simultaneous ECG and KB recordings. Fifty-seven were non-interpretable by the KB. Compared to ECG, the KB interpreted AF with 93% sensitivity, 84% specificity and K coefficient 0.77. Physician-interpretation of KB recordings demonstrated 99% sensitivity, 83% specificity and K coefficient 0.83. Of 57 non-interpretable KB recordings, interpreting electrophysiologists diagnosed AF with 100% sensitivity, 80% specificity and K coefficient 0.74. Among 113 cases where KB and physician readings of the same recording were interpretable, agreement was excellent (K coefficient 0.88). The KB algorithm for AF detection, supported by physician review can accurately differentiate AF from SR. This technology can help screen patients prior to elective CV and avoid unnecessary procedures.

X Demographics

X Demographics

The data shown below were collected from the profiles of 295 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 405 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 405 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 51 13%
Researcher 47 12%
Student > Master 44 11%
Student > Ph. D. Student 33 8%
Other 30 7%
Other 68 17%
Unknown 132 33%
Readers by discipline Count As %
Medicine and Dentistry 106 26%
Engineering 43 11%
Computer Science 29 7%
Nursing and Health Professions 11 3%
Biochemistry, Genetics and Molecular Biology 9 2%
Other 47 12%
Unknown 160 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 228. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2023.
All research outputs
#170,567
of 25,757,133 outputs
Outputs from JACC
#377
of 16,935 outputs
Outputs of similar age
#4,007
of 350,707 outputs
Outputs of similar age from JACC
#12
of 413 outputs
Altmetric has tracked 25,757,133 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 16,935 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.1. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,707 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 413 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.