TY - JOUR
T1 - Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
AU - Discover Consortium (Digital and Noninvasive Screening for COVID-19 with AI ECG Repository)
AU - Attia, Zachi I.
AU - Kapa, Suraj
AU - Dugan, Jennifer
AU - Pereira, Naveen
AU - Noseworthy, Peter A.
AU - Jimenez, Francisco Lopez
AU - Cruz, Jessica
AU - Carter, Rickey E.
AU - DeSimone, Daniel C.
AU - Signorino, John
AU - Halamka, John
AU - Chennaiah Gari, Nikhita R.
AU - Madathala, Raja Sekhar
AU - Platonov, Pyotr G.
AU - Gul, Fahad
AU - Janssens, Stefan P.
AU - Narayan, Sanjiv
AU - Upadhyay, Gaurav A.
AU - Alenghat, Francis J.
AU - Lahiri, Marc K.
AU - Dujardin, Karl
AU - Hermel, Melody
AU - Dominic, Paari
AU - Turk-Adawi, Karam
AU - Asaad, Nidal
AU - Svensson, Anneli
AU - Fernandez-Aviles, Francisco
AU - Esakof, Darryl D.
AU - Bartunek, Jozef
AU - Noheria, Amit
AU - Sridhar, Arun R.
AU - Lanza, Gaetano A.
AU - Cohoon, Kevin
AU - Padmanabhan, Deepak
AU - Pardo Gutierrez, Jose Alberto
AU - Sinagra, Gianfranco
AU - Merlo, Marco
AU - Zagari, Domenico
AU - Rodriguez Escenaro, Brenda D.
AU - Pahlajani, Dev B.
AU - Loncar, Goran
AU - Vukomanovic, Vladan
AU - Jensen, Henrik K.
AU - Farkouh, Michael E.
AU - Luescher, Thomas F.
AU - Su Ping, Carolyn Lam
AU - Peters, Nicholas S.
AU - Friedman, Paul A.
N1 - Funding Information:
We are indebted to all sites that participated without funding in this effort and to Mayo Clinic and the Mayo Clinic Cardiovascular Research Center for resources and support. The work was made possible in part by a philanthropic gift from the Lerer Family Charitable Foundation, Inc. to support a Universal AI Solution for the Early Detection and Diagnosis of Cardiovascular Disease.
Funding Information:
We are indebted to all sites that participated without funding in this effort and to Mayo Clinic and the Mayo Clinic Cardiovascular Research Center for resources and support. The work was made possible in part by a philanthropic gift from the Lerer Family Charitable Foundation, Inc. to support a Universal AI Solution for the Early Detection and Diagnosis of Cardiovascular Disease. Drs Attia and Kapa contributed equally.
Publisher Copyright:
© 2021
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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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U2 - 10.1016/j.mayocp.2021.05.027
DO - 10.1016/j.mayocp.2021.05.027
M3 - Article
C2 - 34353468
AN - SCOPUS:85111611858
VL - 96
SP - 2081
EP - 2094
JO - Mayo Clinic Proceedings
JF - Mayo Clinic Proceedings
SN - 0025-6196
IS - 8
ER -