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Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Nature Medicine volume 28, pages 2497–2503 (2022)Cite this article

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Abstract

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In the present study, a prospective study in which patients of Mayo Clinic were invited by email to download a Mayo Clinic iPhone application that sends watch ECGs to a secure data platform, we examined patient engagement with the study app and the diagnostic utility of the ECGs. We digitally enrolled 2,454 unique patients (mean age 53 ± 15 years, 56% female) from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 16 (3.8%) had an EF ≤ 40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885 (95% confidence interval 0.823–0.946) and 0.881 (0.815–0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively. These findings indicate that consumer watch ECGs, acquired in nonclinical environments, can be used to identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition.

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Fig. 1: Patient enrollment in the remote digital study.
Fig. 2: Study participant engagement with the customized Mayo Clinic iPhone application.
Fig. 3: Assessment of EF using the watch AI-ECG.

Data availability

The data are not publicly available because they are electronic health records. Sharing these data externally without additional consent might compromise patient privacy and would violate the study’s Institutional Review Board approval. If other investigators are interested in performing additional analyses, requests can be made to the corresponding author, P.F., and analyses could be performed in collaboration with the Mayo Clinic.

Code availability

The AI algorithm architecture has been previously published17. The code itself cannot be shared because it contains and is proprietary intellectual property (patent pending) that has been licensed and is under FDA review. We have been advised that, without FDA approval, the AI algorithm cannot be used in routine practice outside the Mayo Clinic.

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Acknowledgements

This publication was made possible through the support of the Ted and Loretta Rogers Cardiovascular Career Development Award Honoring H. C. Smith (to Z.I.A.). The Mayo Clinic CDH funded and developed the iPhone app used in the present study. The ECG dashboard used for clinician review was developed and supported by the Department of Cardiovascular Medicine. P.A.N. receives research funding from the National Institutes of Health (NIH, including the National Heart, Lung, and Blood Institute (grant nos. R21AG 62580-1, R01HL 131535-4 and R01HL 143070-2) and the National Institute on Aging (grant no. R01AG 062436-1)), the Agency for Healthcare Research and Quality (grant no. R01HS 25402-3), the Food and Drug Administration (FDA; grant no. FD 06292) and the American Heart Association (grant no. 18SFRN34230146). D.M.H. receives support from the NIH StARR Resident Investigator Award (grant no. 5R38HL150086-02). No technical or financial support was received from Apple.

Author information

Authors and Affiliations

  1. Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA

    Zachi I. Attia, David M. Harmon, Jennifer Dugan, Francisco Lopez-Jimenez, Amir Lerman, Konstantinos C. Siontis, Peter A. Noseworthy, Xiaoxi Yao, Eric W. Klavetter, Samuel J. Asirvatham & Paul A. Friedman

  2. Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, USA

    David M. Harmon

  3. Center for Digital Health, Mayo Clinic, Rochester, MN, USA

    Lukas Manka, Rita Khan & Bradley C. Leibovich

  4. Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA

    Xiaoxi Yao

  5. Mayo Clinic Platform, Mayo Clinic, Rochester, MN, USA

    John D. Halamka

  6. Department of Quantitative Health Sciences, Jacksonville, FL, USA

    Rickey E. Carter

  7. Department of Urology, Mayo Clinic College of Medicine, Rochester, MN, USA

    Bradley C. Leibovich

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  1. Zachi I. Attia
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Contributions

Z.I.A., J.D., P.A.F., B.L., R.E.C., R.K. and S.A. conceived the project and designed the analysis. Z.I.A., D.M.H., P.A.F., F.L.J., R.E.C., X.Y. and P.N. interpreted the data. Z.I.A., D.M.H., J.D., L.M., F.L.J., P.N., X.Y., R.E.C., S.A., R.K., B.L. and P.A.F. drafted the article or revised it critically for important intellectual content.

Corresponding author

Correspondence to Paul A. Friedman.

Ethics declarations

Competing interests

The AI-ECG algorithm to detect left ventricular dysfunction was licensed by Mayo Clinic to Anumana, Eko health. P.A.F., Z.I.A., F.L.J., R.E.C., S.J.A. and other inventors and advisors to these entities may benefit financially from their commercialization. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Partho Sengupta, Jill Waalen and Mohamed Elshazly for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Patient study invitations by month.

Batch invitations sent to Mayo Clinic app patients on a monthly basis.

Extended Data Fig. 2 EMR-integrated AI Dashboard with Apple Watch ECG data.

Panel A shows the 12 lead ECGs and AI derived scores to multiple AI-ECG models, Panel B shows the watch ECG tracings from the same patient.

Extended Data Fig. 3 Morphological differences between Apple watch and Lead 1 from 12 Lead.

12 Lead ECG recording versus post-processing Apple Watch ECG. Significant high frequency signal loss resulted in visual loss of some characteristics present on the standard 12-lead ECG such as pacing spikes that were absent in the Apple watch ECG.

Extended Data Fig. 4 Patient provider interaction using the AI dashboard.

Case example for patient with new atrial fibrillation on Apple Watch ECG without prior clinical history of atrial fibrillation.

Extended Data Table 1 Clinical characteristics of patients with ventricular dysfunction
Full size table
Extended Data Table 2 Patient comorbidities
Full size table

Supplementary information

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Attia, Z.I., Harmon, D.M., Dugan, J. et al. Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nat Med 28, 2497–2503 (2022). https://doi.org/10.1038/s41591-022-02053-1

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