Literature DB >> 31501201

Diagnostic Accuracy of a Smartphone-Operated, Single-Lead Electrocardiography Device for Detection of Rhythm and Conduction Abnormalities in Primary Care.

Jelle C L Himmelreich1, Evert P M Karregat2, Wim A M Lucassen2, Henk C P M van Weert2, Joris R de Groot3, M Louis Handoko4, Robin Nijveldt5, Ralf E Harskamp2.   

Abstract

PURPOSE: To validate a smartphone-operated, single-lead electrocardiography (1L-ECG) device (AliveCor KardiaMobile) with an integrated algorithm for atrial fibrillation (AF) against 12-lead ECG (12L-ECG) in a primary care population.
METHODS: We recruited consecutive patients who underwent 12L-ECG for any nonacute indication. Patients held a smartphone with connected 1L-ECG while local personnel simultaneously performed 12L-ECG. All 1L-ECG recordings were assessed by blinded cardiologists as well as by the smartphone-integrated algorithm. The study cardiologists also assessed all 12L-recordings in random order as the reference standard. We determined the diagnostic accuracy of the 1L-ECG in detecting AF or atrial flutter (AFL) as well as any rhythm abnormality and any conduction abnormality with the simultaneously performed 12L-ECG as the reference standard.
RESULTS: We included 214 patients from 10 Dutch general practices. Mean ± SD age was 64.1 ± 14.7 years, and 53.7% of the patients were male. The 12L-ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality in 23, 44, and 28 patients, respectively. The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for AF/AFL of 100% (95% CI, 85.2%-100%) and 100% (95% CI, 98.1%-100%). The AF detection algorithm had a sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%). The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for any rhythm abnormality of 90.9% (95% CI, 78.3%-97.5%) and 93.5% (95% CI, 88.7%-96.7%) and for any conduction abnormality of 46.4% (95% CI, 27.5%-66.1%) and 100% (95% CI, 98.0%-100%).
CONCLUSIONS: In a primary care population, a smartphone-operated, 1L-ECG device showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for conduction abnormalities.
© 2019 Annals of Family Medicine, Inc.

Entities:  

Keywords:  atrial fibrillation; cardiac arrhythmia; cardiac complexes, premature; cardiac conduction system disease; electrocardiography; medical device; single-lead

Mesh:

Year:  2019        PMID: 31501201      PMCID: PMC7032908          DOI: 10.1370/afm.2438

Source DB:  PubMed          Journal:  Ann Fam Med        ISSN: 1544-1709            Impact factor:   5.166


  27 in total

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Journal:  Thromb Haemost       Date:  2014-04-01       Impact factor: 5.249

2.  Diagnostic yield of patient-activated loop recorders for detecting heart rhythm abnormalities in general practice: a randomised clinical trial.

Authors:  Emmy Hoefman; Henk C P M van Weert; Johannes B Reitsma; Rudolph W Koster; Patrick J E Bindels
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3.  Management of patients with palpitations: a position paper from the European Heart Rhythm Association.

Authors:  Antonio Raviele; Franco Giada; Lennart Bergfeldt; Jean Jacques Blanc; Carina Blomstrom-Lundqvist; Lluis Mont; John M Morgan; M J Pekka Raatikainen; Gerhard Steinbeck; Sami Viskin; Paulus Kirchhof; Frieder Braunschweig; Martin Borggrefe; Meleze Hocini; Paolo Della Bella; Dipen Chandrakant Shah
Journal:  Europace       Date:  2011-07       Impact factor: 5.214

4.  Head-to-Head Comparison of the AliveCor Heart Monitor and Microlife WatchBP Office AFIB for Atrial Fibrillation Screening in a Primary Care Setting.

Authors:  Pak-Hei Chan; Chun-Ka Wong; Louise Pun; Yu-Fai Wong; Michelle Man-Ying Wong; Daniel Wai-Sing Chu; Chung-Wah Siu
Journal:  Circulation       Date:  2017-01-03       Impact factor: 29.690

5.  Self-monitoring for atrial fibrillation recurrence in the discharge period post-cardiac surgery using an iPhone electrocardiogram.

Authors:  Nicole Lowres; Georgina Mulcahy; Robyn Gallagher; Saul Ben Freedman; David Marshman; Ann Kirkness; Jessica Orchard; Lis Neubeck
Journal:  Eur J Cardiothorac Surg       Date:  2016-02-04       Impact factor: 4.191

6.  Wireless Smartphone ECG Enables Large-Scale Screening in Diverse Populations.

Authors:  Zachary C Haberman; Ryan T Jahn; Rupan Bose; Han Tun; Jerold S Shinbane; Rahul N Doshi; Philip M Chang; Leslie A Saxon
Journal:  J Cardiovasc Electrophysiol       Date:  2015-03-19

7.  Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iREAD Study.

Authors:  Amila D William; Majd Kanbour; Thomas Callahan; Mandeep Bhargava; Niraj Varma; John Rickard; Walid Saliba; Kathy Wolski; Ayman Hussein; Bruce D Lindsay; Oussama M Wazni; Khaldoun G Tarakji
Journal:  Heart Rhythm       Date:  2018-08-22       Impact factor: 6.343

8.  Arrhythmias in general practice: diagnostic value of patient characteristics, medical history and symptoms.

Authors:  P J Zwietering; J A Knottnerus; P E Rinkens; M A Kleijne; A P Gorgels
Journal:  Fam Pract       Date:  1998-08       Impact factor: 2.267

9.  Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

Authors:  Pak-Hei Chan; Chun-Ka Wong; Yukkee C Poh; Louise Pun; Wangie Wan-Chiu Leung; Yu-Fai Wong; Michelle Man-Ying Wong; Ming-Zher Poh; Daniel Wai-Sing Chu; Chung-Wah Siu
Journal:  J Am Heart Assoc       Date:  2016-07-21       Impact factor: 5.501

10.  Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO).

Authors:  Noé Brasier; Christina J Raichle; Marcus Dörr; Adrian Becke; Vivien Nohturfft; Stefan Weber; Fabienne Bulacher; Lorena Salomon; Thierry Noah; Ralf Birkemeyer; Jens Eckstein
Journal:  Europace       Date:  2019-01-01       Impact factor: 5.214

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2.  Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study.

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4.  Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion.

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Journal:  Sensors (Basel)       Date:  2021-02-08       Impact factor: 3.576

Review 5.  Emerging Technologies for Identifying Atrial Fibrillation.

Authors:  Eric Y Ding; Gregory M Marcus; David D McManus
Journal:  Circ Res       Date:  2020-06-18       Impact factor: 23.213

Review 6.  Remote and wearable ECG devices with diagnostic abilities in adults: A state-of-the-science scoping review.

Authors:  Zeineb Bouzid; Salah S Al-Zaiti; Raymond Bond; Ervin Sejdić
Journal:  Heart Rhythm       Date:  2022-03-09       Impact factor: 6.779

Review 7.  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

8.  Single-lead ECGs (AliveCor) are a feasible, cost-effective and safer alternative to 12-lead ECGs in community diagnosis and monitoring of atrial fibrillation.

Authors:  Jonathan James Hyett Bray; Elin Fflur Lloyd; Firdaus Adenwalla; Sarah Kelly; Kathie Wareham; Julian P J Halcox
Journal:  BMJ Open Qual       Date:  2021-03

9.  Manual QT interval measurement with a smartphone-operated single-lead ECG versus 12-lead ECG: a within-patient diagnostic validation study in primary care.

Authors:  Lisa Beers; Lisa P van Adrichem; Jelle C L Himmelreich; Evert P M Karregat; Jonas S S G de Jong; Pieter G Postema; Joris R de Groot; Wim A M Lucassen; Ralf E Harskamp
Journal:  BMJ Open       Date:  2021-11-03       Impact factor: 2.692

10.  Evaluation of general practitioners' single-lead electrocardiogram interpretation skills: a case-vignette study.

Authors:  Evert P M Karregat; Jelle C L Himmelreich; Wim A M Lucassen; Wim B Busschers; Henk C P M van Weert; Ralf E Harskamp
Journal:  Fam Pract       Date:  2021-03-29       Impact factor: 2.267

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