Literature DB >> 29525063

Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation.

Guy Rozen1, Jeena Vaid2, Seyed Mohammadreza Hosseini2, M Ihsan Kaadan2, Allon Rafael2, Attila Roka2, Yukkee C Poh3, Ming-Zher Poh3, Edwin Kevin Heist2, Jeremy Neil Ruskin4.   

Abstract

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, associated with significant morbidity, increased mortality, and rising health-care costs. Simple and available tools for the accurate detection of arrhythmia recurrence in patients after electrical cardioversion (CV) or ablation procedures for AF can help to guide therapeutic decisions. We conducted a prospective, single-center study to evaluate the accuracy of Cardiio Rhythm Mobile Application (CRMA) for AF detection. Patients >18 years of age who were scheduled for elective CV for AF were enrolled in the study. CRMA finger pulse recordings, utilizing an iPhone camera, were obtained before (pre-CV) and after (post-CV) the CV. The findings were validated against surface electrocardiograms. Ninety-eight patients (75.5% men), mean age of 67.7 ± 10.5 years, were enrolled. No electrocardiogram for validation was available in 1 case. Pre-CV CRMA readings were analyzed in 97 of the 98 patients. Post-CV CRMA readings were analyzed for 92 of 93 patients who underwent CV. One patient left before the recording was obtained. The Cardiio Rhythm Mobile Application correctly identified 94 of 101 AF recordings (93.1%) as AF and 80 of 88 non-AF recordings (90.1%) as non-AF. The sensitivity was 93.1% (95% confidence interval [CI] = 86.9% to 97.2%) and the specificity was 90.9% (95% CI = 82.9% to 96.0%). The positive predictive value was 92.2% (95% CI = 85.8% to 95.8%) and the negative predictive value was 92.0% (95% CI = 94.8% to 95.9%). In conclusion, the CRMA demonstrates promising potential in accurate detection and discrimination of AF from normal sinus rhythm in patients with a history of AF.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29525063     DOI: 10.1016/j.amjcard.2018.01.035

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


  13 in total

Review 1.  Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Authors:  Albert K Feeny; Mina K Chung; Anant Madabhushi; Zachi I Attia; Maja Cikes; Marjan Firouznia; Paul A Friedman; Matthew M Kalscheur; Suraj Kapa; Sanjiv M Narayan; Peter A Noseworthy; Rod S Passman; Marco V Perez; Nicholas S Peters; Jonathan P Piccini; Khaldoun G Tarakji; Suma A Thomas; Natalia A Trayanova; Mintu P Turakhia; Paul J Wang
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-07-06

Review 2.  Mobile health solutions for atrial fibrillation detection and management: a systematic review.

Authors:  Astrid N L Hermans; Monika Gawalko; Lisa Dohmen; Rachel M J van der Velden; Konstanze Betz; David Duncker; Dominique V M Verhaert; Hein Heidbuchel; Emma Svennberg; Lis Neubeck; Jens Eckstein; Deirdre A Lane; Gregory Y H Lip; Harry J G M Crijns; Prashanthan Sanders; Jeroen M Hendriks; Nikki A H A Pluymaekers; Dominik Linz
Journal:  Clin Res Cardiol       Date:  2021-09-21       Impact factor: 6.138

3.  Diagnostic accuracy of an algorithm for detecting atrial fibrillation in a wrist-type pulse wave monitor.

Authors:  Tomoyuki Kabutoya; Shinichi Takahashi; Tomonori Watanabe; Yasushi Imai; Kazuhiro Uemoto; Nobuhiko Yasui; Kazuomi Kario
Journal:  J Clin Hypertens (Greenwich)       Date:  2019-08-17       Impact factor: 3.738

4.  Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

Authors:  Daniele Marinucci; Agnese Sbrollini; Ilaria Marcantoni; Micaela Morettini; Cees A Swenne; Laura Burattini
Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

5.  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

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.  The Auxiliary Diagnostic Value of a Novel Wearable Electrocardiogram-Recording System for Arrhythmia Detection: Diagnostic Trial.

Authors:  Shaomin Zhang; Hong Xian; Yi Chen; Yue Liao; Nan Zhang; Xinyu Guo; Ming Yang; Jinhui Wu
Journal:  Front Med (Lausanne)       Date:  2021-06-24

8.  Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

Authors:  Yong-Yan Fan; Yan-Guang Li; Jian Li; Wen-Kun Cheng; Zhao-Liang Shan; Yu-Tang Wang; Yu-Tao Guo
Journal:  JMIR Mhealth Uhealth       Date:  2019-03-05       Impact factor: 4.773

9.  Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study.

Authors:  Hui Zhang; Jie Zhang; Hong-Bao Li; Yi-Xin Chen; Bin Yang; Yu-Tao Guo; Yun-Dai Chen
Journal:  J Med Internet Res       Date:  2019-12-03       Impact factor: 5.428

10.  Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application.

Authors:  Kirstin Aschbacher; Defne Yilmaz; Yaniv Kerem; Stuart Crawford; David Benaron; Jiaqi Liu; Meghan Eaton; Geoffrey H Tison; Jeffrey E Olgin; Yihan Li; Gregory M Marcus
Journal:  Heart Rhythm O2       Date:  2020-04-27
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