Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.
Mayo Clin Proc
; 96(8): 2081-2094, 2021 08.
Article
em En
| MEDLINE
| ID: mdl-34353468
ABSTRACT
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.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Eletrocardiografia
/
COVID-19
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
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Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Mayo Clin Proc
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Mongólia