1. RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study
- Author
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Demilade Adedinsewo, MD, Jennifer Dugan, BA, Patrick W. Johnson, MS, Erika J. Douglass, DrPH, Andrea Carolina Morales-Lara, MD, Mark A. Parkulo, MD, Henry H. Ting, MD, Leslie T. Cooper, MD, Luis R. Scott, MD, Arturo M. Valverde, MD, Deepak Padmanabhan, MBBS, Nicholas S. Peters, MD, Patrik Bachtiger, MBBS, Mihir Kelshiker, MBBS, Francisco Fernandez-Aviles, MD, Felipe Atienza, MD, Taya V. Glotzer, MD, Marc K. Lahiri, MD, Paari Dominic, MD, Zachi I. Attia, PhD, Suraj Kapa, MD, Peter A. Noseworthy, MD, Naveen L. Pereira, MD, Jessica Cruz, MBA, Elie F. Berbari, MD, Rickey E. Carter, PhD, and Paul A. Friedman, MD
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Objective: To evaluate the ability of a neural network to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using point-of-care electrocardiography obtained with a portable device. Patient and Methods: We enrolled 2827 patients in a prospective observational study, from December 10, 2020, through June 4, 2021, to determine the accuracy of a point-of-care, handheld, smartphone-compatible, artificial intelligence–enabled electrocardiography (ECG) (POC AI-ECG) in detecting asymptomatic SARS-CoV-2 infection using a modified version of an existing deep learning model framework trained on 12-lead ECG data. Results: Study participants were 48% (n=1067) female, 79% (n=1749) White, and 7% (n=153) endorsed previous COVID-19 infection. We found the POC AI-ECG algorithm was ineffective for detecting asymptomatic SARS-CoV-2 infection (area under curve, 0.56; 95% CI, 0.46-0.66), failing to adequately discriminate between ECGs performed among participants who tested positive compared to those who tested negative. Conclusion: Contrary to the prior 12-lead ECG study, a POC AI-ECG failed to reliably identify asymptomatic SARS-CoV-2 infection among adults. This study underscores the importance of prospective testing, assuring similar populations, and using similar signals or data when developing AI-ECG tools. Trial registration: clinicaltrials.gov Identifier: NCT04725097
- Published
- 2023
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