Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram

Zachi I. Attia, Suraj Kapa, Jennifer Dugan, Naveen Pereira, Peter A. Noseworthy, Francisco Lopez Jimenez, Jessica Cruz, Rickey E. Carter, Daniel C. DeSimone, John Signorino, John Halamka, Nikhita R. Chennaiah Gari, Raja Sekhar Madathala, Pyotr G. Platonov, Fahad Gul, Stefan P. Janssens, Sanjiv Narayan, Gaurav A. Upadhyay, Francis J. Alenghat, Marc K. LahiriKarl Dujardin, Melody Hermel, Paari Dominic, Karam Turk-Adawi, Nidal Asaad, Anneli Svensson, Francisco Fernandez-Aviles, Darryl D. Esakof, Jozef Bartunek, Amit Noheria, Arun R. Sridhar, Gaetano A. Lanza, Kevin Cohoon, Deepak Padmanabhan, Jose Alberto Pardo Gutierrez, Gianfranco Sinagra, Marco Merlo, Domenico Zagari, Brenda D. Rodriguez Escenaro, Dev B. Pahlajani, Goran Loncar, Vladan Vukomanovic, Henrik K. Jensen, Michael E. Farkouh, Thomas F. Luescher, Carolyn Lam Su Ping, Nicholas S. Peters, Paul A. Friedman, Discover Consortium (Digital and Noninvasive Screening for COVID-19 with AI ECG Repository)

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.

Originalspråkengelska
Sidor (från-till)2081-2094
Antal sidor14
TidskriftMayo Clinic Proceedings
Volym96
Nummer8
DOI
StatusPublished - 2021

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