Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting

Desteghe, Lien; Raymaekers, Zina; Lutin, Mark; Vijgen, Johan; Dilling-Boer, Dagmara; Koopman, Pieter; Schurmans, Joris; Vanduynhoven, Philippe; Dendale, Paul; Heidbüchel, Hein

doi:10.1093/europace/euw025

Cited by 126 publications

(177 citation statements)

References 17 publications

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“…An important issue was that the version of the AliveCor algorithm used by the app during the study was subsequently discovered to have undergone an intentional change by AliveCor. 16 This algorithm provided 95% sensitivity and 99% specificity in this study, which is substantially better than has been reported by Desteghe et al, 17 but was less sensitive than the earlier version of the algorithm (98% sensitivity), although with greater specificity (99% vs. 91%). 10,14 For this study, there was one case of new AF and one case of known AF, both of whom received a 'normal' algorithm interpretation, but were in AF at the time of the screening based on the research cardiologists' review.…”

Section: Discussionsupporting

confidence: 44%

Screening for atrial fibrillation during influenza vaccinations by primary care nurses using a smartphone electrocardiograph (iECG): A feasibility study

Orchard

Lowres

Freedman

et al. 2016

Eur J Prev Cardiolog

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Background: People with unknown atrial fibrillation (AF), who are often asymptomatic, have a substantially increased risk of stroke. Although recommended in European guidelines, AF screening is not routinely performed. Screening at the time of influenza vaccination presents an ideal opportunity to detect AF in large numbers in a primary care medical setting, with an existing annual recall system for patients aged !65 years. Design: Cross-sectional pilot study of handheld smartphone electrocardiogram (iECG) screening to identify unknown AF. Methods: General practices in Sydney, Australia, were recruited during the influenza-vaccination period of April-June 2015. Practice nurses screened patients aged !65 years with a 30-second iECG, which has a validated algorithm for detecting AF in real time. In order to confirm the accuracy of the algorithm, two research cardiologists reviewed de-identified iECGs. In order to explore barriers and enablers, semi-structured interviews were conducted with selected nurses, practice managers and general practitioners. Results: Five general practices were recruited, and 973/2476 (39%) patients attending influenza vaccination were screened. Screening took an average of 5 minutes (range 1.5-10 minutes); however, abnormal iECGs required additional time. Newly identified AF was found in 8/973 patients (0.8%). The sensitivity of the iECG automated algorithm was 95% (95% confidence interval: 83-99%) and the specificity was 99% (95% confidence interval: 98-100%). Screening by practice nurses was well accepted by practice staff. Key enablers were the confidence and competence of nurses and a 'designated champion' to lead screening at the practice. Barriers were practice specific, and mainly related to staff time and funding. Conclusions: Screening with iECG during influenza vaccination by primary care nurses is feasible and well accepted by practice staff. Addressing barriers is likely to increase uptake.

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Section: Discussionsupporting

confidence: 44%

Screening for atrial fibrillation during influenza vaccinations by primary care nurses using a smartphone electrocardiograph (iECG): A feasibility study

Orchard

Lowres

Freedman

et al. 2016

Eur J Prev Cardiolog

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“…The diagnostic sensitivity of the Cardiio Rhythm fingertip photoplethysmography for AF detection was 92.9%, which was higher than that of the AliveCor automated algorithm of 71.4%. The algorithm used in the AliveCor application at that time had been optimized to provide high specificity (95% CI, 99.4%–99.6%) at the expense of sensitivity (95% CI, 66.7%–71.4%), because this device was marketed to individuals rather than health professionals 30, 31. The specificity of the Cardiio Rhythm application (97.7%) was comparable to that of the AliveCor automated algorithm (99.4%).…”

Section: Discussionmentioning

confidence: 99%

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Yan

Lai

Chan

et al. 2018

JAHA

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BackgroundWe aimed to evaluate a novel method of atrial fibrillation (AF) screening using an iPhone camera to detect and analyze photoplethysmographic signals from the face without physical contact by extracting subtle beat‐to‐beat variations of skin color that reflect the cardiac pulsatile signal.Methods and ResultsPatients admitted to the cardiology ward of the hospital for clinical reasons were recruited. Simultaneous facial and fingertip photoplethysmographic measurements were obtained from 217 hospital inpatients (mean age, 70.3±13.9 years; 71.4% men) facing the front camera and with an index finger covering the back camera of 2 independent iPhones before a 12‐lead ECG was recorded. Backdrop and background light intensity was monitored during signal acquisition. Three successive 20‐second (total, 60 seconds) recordings were acquired per patient and analyzed for heart rate regularity by Cardiio Rhythm (Cardiio Inc, Cambridge, MA) smartphone application. Pulse irregularity in ≥1 photoplethysmographic readings or 3 uninterpretable photoplethysmographic readings were considered a positive AF screening result. AF was present on 12‐lead ECG in 34.6% (n=75/217) patients. The Cardiio Rhythm facial photoplethysmographic application demonstrated high sensitivity (95%; 95% confidence interval, 87%–98%) and specificity (96%; 95% confidence interval, 91%–98%) in discriminating AF from sinus rhythm compared with 12‐lead ECG. The positive and negative predictive values were 92% (95% confidence interval, 84%–96%) and 97% (95% confidence interval, 93%–99%), respectively.ConclusionsDetection of a facial photoplethysmographic signal to determine pulse irregularity attributable to AF is feasible. The Cardiio Rhythm smartphone application showed high sensitivity and specificity, with low negative likelihood ratio for AF from facial photoplethysmographic signals. The convenience of a contact‐free approach is attractive for community screening and has the potential to be useful for distant AF screening.

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“…[9] Implementation of mobile devices may provide an opportunity to address the challenges described here and aim to improve outcomes in vulnerable individuals with AF. While smartphone-based technologies are effective for screening and monitoring AF, [10,11] the data regarding their use to address adherence and HRQoL in AF remain sparse.…”

Section: Introductionmentioning

confidence: 99%

Rationale and design of the Atrial Fibrillation health Literacy Information Technology Trial: (AF-LITT)

Guhl

Schlusser

Henault

et al. 2017

Contemporary Clinical Trials

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Background-Atrial Fibrillation (AF) is a common cardiac arrhythmia that is challenging for patients and adversely impacts health-related quality of life (HRQoL). Long-term management of AF requires that patients adhere to complex therapies, understand difficult terminology, navigate subspecialty care, and have continued symptom monitoring with the goal of preventing adverse outcomes. Continued interventions to ameliorate the patient experience of AF are essential.

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Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting

Cited by 126 publications

References 17 publications

Screening for atrial fibrillation during influenza vaccinations by primary care nurses using a smartphone electrocardiograph (iECG): A feasibility study

Screening for atrial fibrillation during influenza vaccinations by primary care nurses using a smartphone electrocardiograph (iECG): A feasibility study

Contact‐Free Screening of Atrial Fibrillation by a Smartphone Using Facial Pulsatile Photoplethysmographic Signals

Rationale and design of the Atrial Fibrillation health Literacy Information Technology Trial: (AF-LITT)

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