Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram

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

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.