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Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity
Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male)...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499801/ https://www.ncbi.nlm.nih.gov/pubmed/37704692 http://dx.doi.org/10.1038/s41598-023-42252-5 |
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author | Baek, Yong-Soo Jo, Yoonsu Lee, Sang-Chul Choi, Wonik Kim, Dae-Hyeok |
author_facet | Baek, Yong-Soo Jo, Yoonsu Lee, Sang-Chul Choi, Wonik Kim, Dae-Hyeok |
author_sort | Baek, Yong-Soo |
collection | PubMed |
description | Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662–0.807) and 0.734 (95% CI: 0.688–0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830–0.835), precision of 0.764 (95% CI: 0.757–0.771), recall of 0.747 (95% CI: 0.741–0.753), and F1 score of 0.747 (95% CI: 0.741–0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156–3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management. |
format | Online Article Text |
id | pubmed-10499801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104998012023-09-15 Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity Baek, Yong-Soo Jo, Yoonsu Lee, Sang-Chul Choi, Wonik Kim, Dae-Hyeok Sci Rep Article Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 patients (mean age: 59.7 ± 20.1 years; 54.2% male) who underwent ECGs at our emergency department before severity classification. The AI-ECG algorithm was evaluated for severity assessment during admission, compared to the Early Warning Scores (EWSs) using the area under the curve (AUC) of the receiver operating characteristic curve, precision, recall, and F1 score. During the internal and external validation, the AI algorithm demonstrated reasonable outcomes in predicting COVID-19 severity with AUCs of 0.735 (95% CI: 0.662–0.807) and 0.734 (95% CI: 0.688–0.781). Combined with EWSs, it showed reliable performance with an AUC of 0.833 (95% CI: 0.830–0.835), precision of 0.764 (95% CI: 0.757–0.771), recall of 0.747 (95% CI: 0.741–0.753), and F1 score of 0.747 (95% CI: 0.741–0.753). In Cox proportional hazards models, the AI-ECG revealed a significantly higher hazard ratio (HR, 2.019; 95% CI: 1.156–3.525, p = 0.014) for mortality, even after adjusting for relevant parameters. Therefore, application of AI-ECG has the potential to assist in early COVID-19 severity prediction, leading to improved patient management. Nature Publishing Group UK 2023-09-13 /pmc/articles/PMC10499801/ /pubmed/37704692 http://dx.doi.org/10.1038/s41598-023-42252-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Baek, Yong-Soo Jo, Yoonsu Lee, Sang-Chul Choi, Wonik Kim, Dae-Hyeok Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title | Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title_full | Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title_fullStr | Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title_full_unstemmed | Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title_short | Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
title_sort | artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499801/ https://www.ncbi.nlm.nih.gov/pubmed/37704692 http://dx.doi.org/10.1038/s41598-023-42252-5 |
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