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RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study
- Source :
- Mayo Clinic Proceedings: Digital Health, Vol 1, Iss 4, Pp 455-466 (2023)
- Publication Year :
- 2023
- Publisher :
- Elsevier, 2023.
-
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
- Subjects :
- Computer applications to medicine. Medical informatics
R858-859.7
Subjects
Details
- Language :
- English
- ISSN :
- 29497612
- Volume :
- 1
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Mayo Clinic Proceedings: Digital Health
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.70df8238488d417d98aee85d5f6a403d
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.mcpdig.2023.07.007