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Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

Nature Medicine volume 25, pages 70–74 (2019)Cite this article

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Abstract

Asymptomatic left ventricular dysfunction (ALVD) is present in 3–6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found1,2,3,4. An inexpensive, noninvasive screening tool for ALVD in the doctor’s office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart’s electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG—a ubiquitous, low-cost test—permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.

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Fig. 1: Creation of the study data sets.
Fig. 2: Network ROC and sensitivity and specificity across age and gender subsets.
Fig. 3: Long-term incidence of developing an EF of ≤35% in patients with an initially normal EF stratified by AI classification.

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Data availability

All requests for raw and analyzed data and related materials, excluding programming code, will be reviewed by the Mayo Clinic legal department and Mayo Clinic Ventures to verify whether the request is subject to any intellectual property or confidentiality obligations. Requests for patient-related data not included in the paper will not be considered. Any data and materials that can be shared will be released via a Material Transfer Agreement.

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Acknowledgements

The study was conceived, funded, and executed entirely by Mayo Clinic. There was no industry support of any kind.

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Authors and Affiliations

  1. Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham & Paul A. Friedman

  2. Business Development, Mayo Clinic, Rochester, MN, USA

    Dorothy J. Ladewig & Gaurav Satam

  3. Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Christopher G. Scott

  4. Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA

    Rickey E. Carter

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  1. Zachi I. Attia
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Contributions

D.J.L. and G.S. contributed to the literature search, study coordination, data management, and data collection. Z.I.A., P.A.F., S.K., and F.L.-J. contributed to the study design. C.G.S., Z.I.A., R.E.C., and F.L.-J. contributed to the data analysis. C.G.S., Z.I.A., R.E.C., P.A.N., P.A.P., M.E.-S., P.A.F., S.K., P.M.M., T.M.M., and S.J.A. contributed to data interpretation. Z.I.A., R.E.C., F.L.-J., P.A.F., P.A.N., and P.A.P. contributed to the writing of the manuscript. R.E.C., P.A.N., P.A.P., M.E.-S., F.L.-J., S.K., P.M.M., T.M.M., and S.J.A contributed to the critical review and editing.

Corresponding author

Correspondence to Paul A. Friedman.

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Competing interests

Mayo Clinic has licensed the underlying technology to EKO, a maker of digital stethoscopes with embedded ECG electrodes. Mayo Clinic may receive financial benefit from the use of this technology, but at no point will Mayo Clinic benefit financially from its use for the care of patients at Mayo Clinic. P.A.F., F.L.-J., S.K., and Z.I.A. may also receive financial benefit from this agreement.

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Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25, 70–74 (2019). https://doi.org/10.1038/s41591-018-0240-2

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