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Attia ZI, Kapa S, Dugan J, Pereira N, Noseworthy PA, Jimenez FL, Cruz J, Carter RE, DeSimone DC, Signorino J, Halamka J, Chennaiah Gari NR, Madathala RS, Platonov PG, Gul F, Janssens SP, Narayan S, Upadhyay GA, Alenghat FJ, Lahiri MK, Dujardin K, Hermel M, Dominic P, Turk-Adawi K, Asaad N, Svensson A, Fernandez-Aviles F, Esakof DD, Bartunek J, Noheria A, Sridhar AR, Lanza GA, Cohoon K, Padmanabhan D, Pardo Gutierrez JA, Sinagra G, Merlo M, Zagari D, Rodriguez Escenaro BD, Pahlajani DB, Loncar G, Vukomanovic V, Jensen HK, Farkouh ME, Luescher TF, Su Ping CL, Peters NS, Friedman PA. Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram. Mayo Clin Proc 2021; 96:2081-2094. [PMID: 34353468 PMCID: PMC8327278 DOI: 10.1016/j.mayocp.2021.05.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS A 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. RESULTS The 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%. CONCLUSION Infection 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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Key Words
- ace2, angiotensin-converting enzyme 2
- ai, artificial intelligence
- ai-ecg, artificial intelligence–enhanced electrocardiogram
- auc, area under the curve
- covid-19, coronavirus infectious disease 19
- npv, negative predictive value
- pcr, polymerase chain reaction
- ppv, positive predictive value
- redcap, research electronic data capture
- sars-cov-2, severe acute respiratory syndrome coronavirus 2
- who, world health organization
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Jennifer Dugan
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Naveen Pereira
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | | | - Jessica Cruz
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL
| | - Daniel C DeSimone
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN; Division of Infectious Diseases, Mayo Clinic College of Medicine, Rochester, MN
| | - John Signorino
- Department of Compliance, Mayo Clinic College of Medicine, Rochester, MN
| | - John Halamka
- Mayo Clinic Platform, Mayo Clinic College of Medicine, Rochester, MN
| | | | | | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Fahad Gul
- Division of Cardiology, Heart and Vascular Institute, Einstein Healthcare Network, Philadelphia, PA
| | - Stefan P Janssens
- Department of Cardiovascular Diseases, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Sanjiv Narayan
- Cardiovascular Institute and Department of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA
| | - Gaurav A Upadhyay
- Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL
| | - Francis J Alenghat
- Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL
| | - Marc K Lahiri
- Henry Ford Hospital, Heart and Vascular Institute, Detroit, MI
| | - Karl Dujardin
- Department of Cardiology, AZ Delta Hospital, AZ Delta Campus Rumbeke, Deltalaan, Belgium
| | - Melody Hermel
- Scripps Health and the Scripps Clinic Division of Cardiology, La Jolla, CA
| | - Paari Dominic
- Louisiana State University Health Sciences Center, Shreveport, LA
| | | | | | - Anneli Svensson
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University Hospital, Linköping, Sweden
| | - Francisco Fernandez-Aviles
- Hospital General Universitario Gregorio Maranon, Instituto de Investigacion Sanitaria Gregorio Maranon, Universidad Complutense, Madrid, Spain
| | - Darryl D Esakof
- Department of Cardiology, Lahey Hospital & Medical Center, Burlington, MA
| | | | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Health System, Kansas City, KS
| | - Arun R Sridhar
- Section of Cardiac Electrophysiology, University of Washington Medical Center, Seattle, WA
| | - Gaetano A Lanza
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Universita Cattolica del Sacro Cuore, Cardiology Institute, Rome, Italy
| | - Kevin Cohoon
- Division of Cardiovascular Medicine Froedtert & the Medical College of Wisconsin, Milwaukee, WI
| | - Deepak Padmanabhan
- Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bangalore, India
| | | | - Gianfranco Sinagra
- Cardiovascular Department "Ospedali Riuniti" and University of Trieste, Trieste, Italy
| | - Marco Merlo
- Cardiovascular Department "Ospedali Riuniti" and University of Trieste, Trieste, Italy
| | - Domenico Zagari
- Electrophysiology and Cardiac Pacing Unit, Humanitas Mater Domini Clinical Institute, Castellanza, Italy
| | | | | | - Goran Loncar
- Department of Cardiology, Institute for Cardiovascular Diseases Dedinje (ICVDD), Belgrade, Serbia
| | - Vladan Vukomanovic
- University Hospital Center "Dr Dragisa Misovic-Dedinje", Belgrade, Serbia
| | - Henrik K Jensen
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | | | | | | | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN.
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