Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Pérez, Marco; Mahaffey, Kenneth W.; Hedlin, Haley; Rumsfeld, John S.; García, Ariadna; Ferris, Todd; Balasubramanian, Vidhya; Russo, Andrea M.; Rajmane, Amol; Cheung, Lauren; Hung, Grace; Lee, Justin; Kowey, Peter R.; Talati, Nisha; Nag, Divya; Gummidipundi, Santosh; Beatty, Alexis L.; Hills, Mellanie True; Desai, Sumbul; Granger, Christopher B.; Desai, Manisha; Turakhia, Mintu P.; Investigators, Apple Heart Study

doi:10.1056/nejmoa1901183

Cited by 1,269 publications

(938 citation statements)

References 12 publications

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“…Studies using non-medical devices such as smart watches or other commercial wearable devices are also actively underway. A study was conducted recently in the United States on 419,297 members with no history of atrial fibrillation who agreed to being monitored by a smartphone (Apple iPhone) app [27]. The study showed that 84% of the notifications from the irregular pulse notification algorithm were concordant with atrial fibrillation.…”

Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

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Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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“…The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation [26]. The results are very promising as with advances in technology diagnosing silent AF could be accomplished with technology that we use on a daily basis.…”

Section: Discussionmentioning

confidence: 88%

Frequency of de novo atrial fibrillation in patients presenting with acute ischemic cerebrovascular stroke

et al. 2020

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Background: Atrial fibrillation (AF) affects millions of people worldwide and can remain undiagnosed for years. It is a major cause of cerebrovascular stroke (CVS); hence, early detection is extremely important in order to decrease the risk of CVS. We conducted a retrospective observational study looking into the prevalence of silent AF in 3299 patients admitted from January 2014 to December 2017 in a tertiary care stroke specialized center. Ischemic CVS was confirmed either by using multislice computed tomography (MSCT) or magnetic resonance imaging (MRI) of the brain. AF was diagnosed by electrocardiography (ECG) at the time of admission or during the hospital stay. Patients with a history of AF were excluded from the study.Results: Of the 3299 patients admitted by acute ischemic CVS, 707 (21.43%) had a history of AF and thus were excluded from the study. Of the remaining 2592 patients eligible for the study, 1666 (64.27%) were males with a mean age of 56.06 years (± 16.01). A total of 2313 (89.24%) patients remained in sinus rhythm throughout the hospital stay, 211 (8.14%) patients were in AF on admission, and 68 (2.62 %) patients developed AF during their hospital stay. The total number of newly diagnosed patients with AF was 279 (10.76%). Conclusion:The prevalence of de novo atrial fibrillation in patients presented with acute cerebrovascular stroke is high. The implementation of good screening programs can significantly reduce the risk of disabilities and morbidities.

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“…Eines der am intensivsten diskutierten Projekte der vergangenen Jahre ist sicherlich die Apple Heart Study [7]. Für diese Studie wurden innerhalb von nur 8 Monaten insgesamt >419.000 Besitzer Abb.…”

Section: » Wir Werden Vermehrt Mitunclassified

Neuigkeiten Elektrophysiologie

2020

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Exemplarisch seien hier 2 Arbeitsgruppen herausgegriffen, um darzustellen, wie häufig bereits der Bezug von der Grundlagenforschung in den klinischen Alltag hergestellt werden kann: zum einen die Gruppe um Joseph Hill aus Dallas, Texas. Diese Gruppe hat im vergangenen Jahr die "2-hit hypothesis" zur Entstehung der Herzinsuffizienz mit erhaltener LV(linksventrikulärer)-Funktion (HFpEF) vorgestellt und im Nature Journal [1] hochrangig publiziert. Die "2-hit hypothesis" besagt, dass das menschliche Herz erst beim Zusammenwirken mehrerer Faktoren eine HFpEF entwickelt. Die eine Voraussetzung ist mechanischer Stress wie z. B. im Rahmen einer hypertensiven Herzerkrankung, zum anderen wird ein metabolischer Stress benötigt wie z. B. im Rahmen eines Diabetes oder einer Adipositas. Es gelang, ein Mausmodell zu entwickeln, in dem durch diese beiden Faktoren eine schwere HFpEF induziert werden konnte mit deutlicher diastolischer Compliancestörung, aber erhaltener globaler Pumpfunktion. Mit Hilfe eines Knockout -Mausmodells konnte dann die Bedeutung des oxidativen Stresses belegt werden, da die Knockout-Tiere für iNOS (induzierbare Stickstoffmonoxid-Synthase) eine signifikant abgeschwächte Form der HFpEF entwickelten im Vergleich zu den Wildtyp-Mäusen. Somit generierte diese Forschergruppe elementare Erkenntnisse zur Entwicklung einer HFpEF. Eine weitere Gruppe, die hier exemplarisch genannt werden soll, ist die

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Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Cited by 1,269 publications

References 12 publications

Discovering hidden information in biosignals from patients using artificial intelligence

Discovering hidden information in biosignals from patients using artificial intelligence

Frequency of de novo atrial fibrillation in patients presenting with acute ischemic cerebrovascular stroke

Neuigkeiten Elektrophysiologie

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