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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
Published on: December 11, 2019
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Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Zachi I Attia1, David M Harmon1,2, Jennifer Dugan1
1Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA.
Nature Medicine
|November 14, 2022
Summary
Artificial intelligence (AI) can now detect cardiac dysfunction using smartwatch electrocardiograms (ECGs). This study shows consumer watch ECGs can identify patients with reduced ejection fraction (EF) in nonclinical settings.
Area of Science:
- Cardiology
- Artificial Intelligence
- Digital Health
Background:
- Artificial intelligence (AI) algorithms can identify cardiac dysfunction from 12-lead ECGs.
- Smartwatch-based single-lead ECGs for cardiac dysfunction detection remain untested.
Purpose of the Study:
- To examine patient engagement with a smartwatch ECG app.
- To assess the diagnostic utility of smartwatch ECGs for identifying cardiac dysfunction (ejection fraction ≤ 40%).
Main Methods:
- Prospective study enrolling 2,454 patients via a Mayo Clinic iPhone app.
- Collected 125,610 smartwatch ECGs from August 2021 to February 2022.
- Validated AI algorithm performance against echocardiogram-determined ejection fraction (EF) in 421 participants.
Main Results:
- AI algorithm achieved an area under the curve (AUC) of 0.885 for detecting low EF using mean prediction within a 30-day window.
- AUC was 0.881 when using the closest ECG relative to the echocardiogram.
- 3.8% of participants with available ECG and echocardiogram data had an EF ≤ 40%.
Conclusions:
- Consumer smartwatch ECGs, even from nonclinical settings, can effectively identify patients with cardiac dysfunction.
- This technology offers a potential tool for detecting a life-threatening, often asymptomatic condition.
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