Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.
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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, Discover Consortium (Digital and Noninvasive Screening for COVID-19 with AI ECG Repository)
Аннотация
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.