TY - JOUR
T1 - RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram
T2 - A Prospective Observational Study
AU - Adedinsewo, Demilade
AU - Dugan, Jennifer
AU - Johnson, Patrick W.
AU - Douglass, Erika J.
AU - Morales-Lara, Andrea Carolina
AU - Parkulo, Mark A.
AU - Ting, Henry H.
AU - Cooper, Leslie T.
AU - Scott, Luis R.
AU - Valverde, Arturo M.
AU - Padmanabhan, Deepak
AU - Peters, Nicholas S.
AU - Bachtiger, Patrik
AU - Kelshiker, Mihir
AU - Fernandez-Aviles, Francisco
AU - Atienza, Felipe
AU - Glotzer, Taya V.
AU - Lahiri, Marc K.
AU - Dominic, Paari
AU - Attia, Zachi I.
AU - Kapa, Suraj
AU - Noseworthy, Peter A.
AU - Pereira, Naveen L.
AU - Cruz, Jessica
AU - Berbari, Elie F.
AU - Carter, Rickey E.
AU - Friedman, Paul A.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/12
Y1 - 2023/12
N2 - 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
AB - 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
UR - https://www.scopus.com/pages/publications/85204304124
U2 - 10.1016/j.mcpdig.2023.07.007
DO - 10.1016/j.mcpdig.2023.07.007
M3 - Article
AN - SCOPUS:85204304124
SN - 2949-7612
VL - 1
SP - 455
EP - 466
JO - Mayo Clinic Proceedings: Digital Health
JF - Mayo Clinic Proceedings: Digital Health
IS - 4
ER -