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A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study
Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-1...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617603/ https://www.ncbi.nlm.nih.gov/pubmed/36309538 http://dx.doi.org/10.1038/s41598-022-23285-8 |
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author | Wang, Yiting Li, Xuewen Xu, Jiancheng Zhou, Qi |
author_facet | Wang, Yiting Li, Xuewen Xu, Jiancheng Zhou, Qi |
author_sort | Wang, Yiting |
collection | PubMed |
description | Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284–12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088–1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261–5.297), and platelet distribution width (OR 1.559; 95% CI 1.154–2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19. |
format | Online Article Text |
id | pubmed-9617603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96176032022-10-31 A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study Wang, Yiting Li, Xuewen Xu, Jiancheng Zhou, Qi Sci Rep Article Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284–12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088–1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261–5.297), and platelet distribution width (OR 1.559; 95% CI 1.154–2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19. Nature Publishing Group UK 2022-10-29 /pmc/articles/PMC9617603/ /pubmed/36309538 http://dx.doi.org/10.1038/s41598-022-23285-8 Text en © The Author(s) 2022 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 Wang, Yiting Li, Xuewen Xu, Jiancheng Zhou, Qi A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title | A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title_full | A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title_fullStr | A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title_full_unstemmed | A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title_short | A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study |
title_sort | complete blood count-based multivariate model for predicting the recovery of patients with moderate covid-19: a retrospective study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617603/ https://www.ncbi.nlm.nih.gov/pubmed/36309538 http://dx.doi.org/10.1038/s41598-022-23285-8 |
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