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

William, A.; Kanbour, Majd; Callahan, Thomas; Bhargava, Mandeep; Varma, Niraj; Rickard, John; Saliba, Walid; Wolski, Kathy; Hussein, Ayman A.; Lindsay, Bruce D.; Wazni, Oussama; Tarakji, Khaldoun G.

doi:10.1016/j.hrthm.2018.06.037

Cited by 147 publications

(114 citation statements)

References 11 publications

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“…The positive predictive value of "unclassified" rhythm was reported to be 34% in that study. 17 Certainly, in a population without known AF where the true prevalence of disease is lower, the positive predictive value would be expected to be lower as well. Nevertheless, using these reported values, our screening program could have identified as many as 44 cases of AF in the total screened population of 772.…”

Section: Discussionmentioning

confidence: 99%

The Heart Rhythm Society/American College of Physicians Atrial Fibrillation Screening and Education Initiative

et al. 2019

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BACKGROUND The prevalence of both atrial fibrillation (AF) and stroke is increasing. Stroke is common in AF and can have devastating consequences, especially when AF is unrecognized and anticoagulation is not initiated.OBJECTIVE The purpose of this study was to demonstrate the feasibility and yield, both in identifying previously undiagnosed AF and in educating patients and caregivers about AF, of systematic screening events in internal medicine practices using a mobile electrocardiogram device (Kardia/AliveCor iECG).METHODS With support from the Heart Rhythm Society and the American College of Physicians, 5 internal medicine practices performed systematic screening and education of patients at higher risk of AF using the Kardia/AliveCor device and a variety of educational materials. Patients screened as "unclassified" or "possible AF" were referred for further evaluation. Patients and providers (physicians, nurses, and allied professionals) assessed the screening process.

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Section: Discussionmentioning

confidence: 99%

The Heart Rhythm Society/American College of Physicians Atrial Fibrillation Screening and Education Initiative

et al. 2019

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“…Some products, like the KardiaMobile and KardiaBand, have developed algorithms to analyze the ECG recording to detect arrhythmias. AliveCor's atrial fibrillation detection algorithm was shown to have a sensitivity and specificity of 96.6% and 94%, respectively, when used with the KardiaMobile . When used with the KardiaBand, the algorithm demonstrated a sensitivity and specificity of 93% and 84%, respectively .…”

Section: Four System Architectures Of Biosensors For Personal Mhealthmentioning

confidence: 99%

Biosensors for Personal Mobile Health: A System Architecture Perspective

Arumugam

Colburn

Sia

2019

Adv Materials Technologies

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Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor‐based sensors are enabling measurement of vital signs, and microfluidic‐based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it can become critical for researchers to design biosensors—with appropriate functionalities and specifications—to work seamlessly with accompanying connected hardware and software. Here, recent research in biosensors, as well as current mobile health devices in use is described and classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. The path forward for integrating biosensors into smartphone‐based mobile health devices is also discussed.

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“…In clinical trials, the device has proven effi cacy in identifying atrial fi brillation, with a sensitivity of 96.6% and specifi city of 94.1% (William et al, 2018). Th is was, however, aft er the exclusion of unclassifi ed results, which are recordings that the device cannot interpret.…”

Section: Kardiamobilementioning

confidence: 99%