Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram |
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Affiliation: | 1. Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN;2. Division of Infectious Diseases, Mayo Clinic College of Medicine, Rochester, MN;3. Department of Compliance, Mayo Clinic College of Medicine, Rochester, MN;4. Mayo Clinic Platform, Mayo Clinic College of Medicine, Rochester, MN;5. Department of Hepatology and Transplant, Mayo Clinic College of Medicine, Rochester, MN;6. Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL;7. Department of Internal Medicine, Mayo Clinic College of Medicine, Austin, MN;8. Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden;9. Division of Cardiology, Heart and Vascular Institute, Einstein Healthcare Network, Philadelphia, PA;10. Department of Cardiovascular Diseases, University Hospitals Leuven, KU Leuven, Leuven, Belgium;11. Cardiovascular Institute and Department of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA;12. Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL;13. Henry Ford Hospital, Heart and Vascular Institute, Detroit, MI;14. Department of Cardiology, AZ Delta Hospital, AZ Delta Campus Rumbeke, Deltalaan, Belgium;15. Scripps Health and the Scripps Clinic Division of Cardiology, La Jolla, CA;p. Louisiana State University Health Sciences Center, Shreveport, LA;q. Qatar University, QU-Health, Doha, Qatar;r. Department of Cardiology and Department of Medical and Health Sciences, Linköping University Hospital, Linköping, Sweden;s. Hospital General Universitario Gregorio Maranon, Instituto de Investigacion Sanitaria Gregorio Maranon, Universidad Complutense, Madrid, Spain;t. Department of Cardiology, Lahey Hospital & Medical Center, Burlington, MA;u. Cardiovascular Center, Aalst, OLV Hospital, Belgium;v. Department of Cardiovascular Medicine, The University of Kansas Health System, Kansas City, KS;w. Section of Cardiac Electrophysiology, University of Washington Medical Center, Seattle, WA;x. Fondazione Policlinico Universitario A. Gemelli IRCCS, Universita Cattolica del Sacro Cuore, Cardiology Institute, Rome, Italy;y. Division of Cardiovascular Medicine Froedtert & the Medical College of Wisconsin, Milwaukee, WI;z. Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bangalore, India;11. Clinica Santa Maria, Santiago, Chile;22. Cardiovascular Department “Ospedali Riuniti” and University of Trieste, Trieste, Italy;33. Electrophysiology and Cardiac Pacing Unit, Humanitas Mater Domini Clinical Institute, Castellanza, Italy;44. Medica Sur, Toriello Guerra, Mexico;55. Breach Candy Hospital Trust, Mumbai, Maharashtra, India;66. Department of Cardiology, Institute for Cardiovascular Diseases Dedinje (ICVDD), Belgrade, Serbia;77. University Hospital Center “Dr Dragisa Misovic-Dedinje”, Belgrade, Serbia;88. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark;99. Department of Medicine, University of Toronto, Toronto, Canada;1010. Royal Brompton and Harefield Hospitals, London, United Kingdom;1111. National Heart Centre, Singapore, and Duke–National University of Singapore;1212. National Heart and Lung Institute, Imperial College London, London, United Kingdom;1313. Hamad Medical Corporation, Doha, Qatar |
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Abstract: | ObjectiveTo rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).MethodsA global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction–confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.ResultsThe area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.ConclusionInfection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence–enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control. |
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