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Finding of the factors affecting the severity of COVID-19 based on mathematical models

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is cha...

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Autores principales: Qu, Jiahao, Sumali, Brian, Lee, Ho, Terai, Hideki, Ishii, Makoto, Fukunaga, Koichi, Mitsukura, Yasue, Nishimura, Toshihiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688457/
https://www.ncbi.nlm.nih.gov/pubmed/34930966
http://dx.doi.org/10.1038/s41598-021-03632-x
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author Qu, Jiahao
Sumali, Brian
Lee, Ho
Terai, Hideki
Ishii, Makoto
Fukunaga, Koichi
Mitsukura, Yasue
Nishimura, Toshihiko
author_facet Qu, Jiahao
Sumali, Brian
Lee, Ho
Terai, Hideki
Ishii, Makoto
Fukunaga, Koichi
Mitsukura, Yasue
Nishimura, Toshihiko
author_sort Qu, Jiahao
collection PubMed
description Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.
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spelling pubmed-86884572021-12-22 Finding of the factors affecting the severity of COVID-19 based on mathematical models Qu, Jiahao Sumali, Brian Lee, Ho Terai, Hideki Ishii, Makoto Fukunaga, Koichi Mitsukura, Yasue Nishimura, Toshihiko Sci Rep Article Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity. Nature Publishing Group UK 2021-12-20 /pmc/articles/PMC8688457/ /pubmed/34930966 http://dx.doi.org/10.1038/s41598-021-03632-x Text en © The Author(s) 2021 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
Qu, Jiahao
Sumali, Brian
Lee, Ho
Terai, Hideki
Ishii, Makoto
Fukunaga, Koichi
Mitsukura, Yasue
Nishimura, Toshihiko
Finding of the factors affecting the severity of COVID-19 based on mathematical models
title Finding of the factors affecting the severity of COVID-19 based on mathematical models
title_full Finding of the factors affecting the severity of COVID-19 based on mathematical models
title_fullStr Finding of the factors affecting the severity of COVID-19 based on mathematical models
title_full_unstemmed Finding of the factors affecting the severity of COVID-19 based on mathematical models
title_short Finding of the factors affecting the severity of COVID-19 based on mathematical models
title_sort finding of the factors affecting the severity of covid-19 based on mathematical models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688457/
https://www.ncbi.nlm.nih.gov/pubmed/34930966
http://dx.doi.org/10.1038/s41598-021-03632-x
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