Literature DB >> 30131106

Screening for Atrial Fibrillation Using Economical and Accurate Technology (From the SAFETY Study).

Mark Lown1, Arthur M Yue2, Benoy N Shah2, Simon J Corbett2, George Lewith3, Beth Stuart3, James Garrard3, Michael Brown4, Paul Little3, Michael Moore3.   

Abstract

The prevalence of atrial fibrillation (AF) is estimated at more than 3% in the adult population and there has been increased interest in screening for AF. In the SAFETY trial we chose to evaluate if inexpensive, wearable, consumer electrocardiography (ECG) sensing devices (Polar-H7 [PH7] and Firstbeat Bodyguard 2 [BG2]), could be used to detect AF accurately. We undertook a case-control study of 418 participants aged >65 (82 with AF and/or flutter at the study visit and 336 without) attending 3 general practice surgeries in Hampshire, UK for a single screening visit. The PH7 and BG2 devices were tested alongside 2 established AF detection devices (AliveCor and WatchBP) in random order and the diagnosis of AF was confirmed by 12-Lead ECG interpreted by a panel of cardiologists. The sensitivity (95% confidence interval [CI] range), specificity (95% CI range), and overall accuracy (95% CI range) of the 4 devices were: AliveCor: 87.8% (78.7% to 94.0%), 98.8% (97.0% to 99.7%), 96.7% (94.4% to 98.2%); WatchBP: 96.3% (89.7% to 99.2%), 93.5% (90.3% to 95.9%), 94.0% (91.3% to 96.1%): PH7: 96.3% (89.7% to 99.2%), 98.2% (96.2% to 99.3%), 97.9% (96.0% to 99.0%). BG2: 96.3% (89.7% to 99.2%), 98.5% (96.6% to 99.5%), 98.1% (96.3% to 99.2%). The PH7 and BG2 devices were highly reliable (the devices acquired sufficient data and obtained a diagnostic result in all but 1 participant on the first attempt). In conclusion, inexpensive, consumer heart rate monitoring devices (PH7 and BG2) can be used to detect AF accurately with sensitivity and specificity >95%. The consumer devices performed as well or better than WatchBP and AliveCor and have the capability to store or transmit ECG data which could be used to confirm AF.
Copyright © 2018. Published by Elsevier Inc.

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Year:  2018        PMID: 30131106     DOI: 10.1016/j.amjcard.2018.07.003

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  12 in total

1.  Distinguishing atrial fibrillation from sinus rhythm using commercial pulse detection systems: The non-interventional BAYathlon study.

Authors:  Christian Müller; Ulf Hengstmann; Michael Fuchs; Martin Kirchner; Frank Kleinjung; Harald Mathis; Stephan Martin; Ingo Bläse; Stefan Perings
Journal:  Digit Health       Date:  2021-05-22

2.  Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review.

Authors:  Godwin Denk Giebel; Christian Gissel
Journal:  JMIR Mhealth Uhealth       Date:  2019-06-16       Impact factor: 4.773

3.  The RITMIA™ Smartphone App for Automated Detection of Atrial Fibrillation: Accuracy in Consecutive Patients Undergoing Elective Electrical Cardioversion.

Authors:  Claudio Reverberi; Granit Rabia; Fabrizio De Rosa; Davide Bosi; Andrea Botti; Giorgio Benatti
Journal:  Biomed Res Int       Date:  2019-07-02       Impact factor: 3.411

Review 4.  Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.

Authors:  Chayakrit Krittanawong; Albert J Rogers; Kipp W Johnson; Zhen Wang; Mintu P Turakhia; Jonathan L Halperin; Sanjiv M Narayan
Journal:  Nat Rev Cardiol       Date:  2020-10-09       Impact factor: 32.419

5.  Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study.

Authors:  Liisa Kuula; Anu-Katriina Pesonen
Journal:  JMIR Mhealth Uhealth       Date:  2021-02-03       Impact factor: 4.773

Review 6.  Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis.

Authors:  Tien Yun Yang; Li Huang; Shwetambara Malwade; Chien-Yi Hsu; Yang Ching Chen
Journal:  JMIR Mhealth Uhealth       Date:  2021-04-09       Impact factor: 4.773

7.  Performance of a Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Screening in a Semirural African Population: Insights From "The Heart of Ethiopia: Focus on Atrial Fibrillation" (TEFF-AF) Study.

Authors:  Bradley M Pitman; Sok-Hui Chew; Christopher X Wong; Amenah Jaghoori; Shinsuke Iwai; Gijo Thomas; Andrew Chew; Prashanthan Sanders; Dennis H Lau
Journal:  JMIR Mhealth Uhealth       Date:  2021-05-19       Impact factor: 4.773

8.  HRS White Paper on Clinical Utilization of Digital Health Technology.

Authors:  Elaine Y Wan; Hamid Ghanbari; Nazem Akoum; Zachi Itzhak Attia; Samuel J Asirvatham; Eugene H Chung; Lilas Dagher; Sana M Al-Khatib; G Stuart Mendenhall; David D McManus; Rajeev K Pathak; Rod S Passman; Nicholas S Peters; David S Schwartzman; Emma Svennberg; Khaldoun G Tarakji; Mintu P Turakhia; Anthony Trela; Hirad Yarmohammadi; Nassir F Marrouche
Journal:  Cardiovasc Digit Health J       Date:  2021-07-10

9.  Patients' views about screening for atrial fibrillation (AF): a qualitative study in primary care.

Authors:  Mark Lown; Christopher R Wilcox; Stephanie Hughes; Miriam Santer; George Lewith; Michael Moore; Paul Little
Journal:  BMJ Open       Date:  2020-03-18       Impact factor: 2.692

10.  Machine learning detection of Atrial Fibrillation using wearable technology.

Authors:  Mark Lown; Michael Brown; Chloë Brown; Arthur M Yue; Benoy N Shah; Simon J Corbett; George Lewith; Beth Stuart; Michael Moore; Paul Little
Journal:  PLoS One       Date:  2020-01-24       Impact factor: 3.240

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