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CD8(+) T cells predicted the conversion of common covid-19 to severe

To evaluate the predictive effect of T-lymphoid subsets on the conversion of common covid-19 to severe. The laboratory data were collected retrospectively from common covid-19 patients in the First People's Hospital of Zaoyang, Hubei Province, China and the Third People's Hospital of Kunmi...

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Autores principales: Liu, Li, Chen, Zhiyong, Du, Yingrong, Gao, Jianpeng, Li, Junyi, Deng, Tiqin, Chen, Chen, Wang, Lin, Yang, Yongrui, Liu, Chunyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838185/
https://www.ncbi.nlm.nih.gov/pubmed/33500507
http://dx.doi.org/10.1038/s41598-021-81732-4
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author Liu, Li
Chen, Zhiyong
Du, Yingrong
Gao, Jianpeng
Li, Junyi
Deng, Tiqin
Chen, Chen
Wang, Lin
Yang, Yongrui
Liu, Chunyun
author_facet Liu, Li
Chen, Zhiyong
Du, Yingrong
Gao, Jianpeng
Li, Junyi
Deng, Tiqin
Chen, Chen
Wang, Lin
Yang, Yongrui
Liu, Chunyun
author_sort Liu, Li
collection PubMed
description To evaluate the predictive effect of T-lymphoid subsets on the conversion of common covid-19 to severe. The laboratory data were collected retrospectively from common covid-19 patients in the First People's Hospital of Zaoyang, Hubei Province, China and the Third People's Hospital of Kunming, Yunnan Province, China, between January 20, 2020 and March 15, 2020 and divided into training set and validation set. Univariate and multivariate logistic regression was performed to investigate the risk factors for the conversion of common covid-19 to severe in the training set, the prediction model was established and verified externally in the validation set. 60 (14.71%) of 408 patients with common covid-19 became severe in 6–10 days after diagnosis. Univariate and multiple logistic regression analysis revealed that lactate (P = 0.042, OR = 1097.983, 95% CI 1.303, 924,798.262) and CD8(+) T cells (P = 0.010, OR = 0.903, 95% CI 0.835, 0.975) were independent risk factors for general type patients to turn to severe type. The area under ROC curve of lactate and CD8(+) T cells was 0.754 (0.581, 0.928) and 0.842 (0.713, 0.970), respectively. The actual observation value was highly consistent with the prediction model value in curve fitting. The established prediction model was verified in 78 COVID-19 patients in the verification set, the area under the ROC curve was 0.906 (0.861, 0.981), and the calibration curve was consistent. CD8(+) T cells, as an independent risk factor, could predict the transition from common covid-19 to severe.
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spelling pubmed-78381852021-01-27 CD8(+) T cells predicted the conversion of common covid-19 to severe Liu, Li Chen, Zhiyong Du, Yingrong Gao, Jianpeng Li, Junyi Deng, Tiqin Chen, Chen Wang, Lin Yang, Yongrui Liu, Chunyun Sci Rep Article To evaluate the predictive effect of T-lymphoid subsets on the conversion of common covid-19 to severe. The laboratory data were collected retrospectively from common covid-19 patients in the First People's Hospital of Zaoyang, Hubei Province, China and the Third People's Hospital of Kunming, Yunnan Province, China, between January 20, 2020 and March 15, 2020 and divided into training set and validation set. Univariate and multivariate logistic regression was performed to investigate the risk factors for the conversion of common covid-19 to severe in the training set, the prediction model was established and verified externally in the validation set. 60 (14.71%) of 408 patients with common covid-19 became severe in 6–10 days after diagnosis. Univariate and multiple logistic regression analysis revealed that lactate (P = 0.042, OR = 1097.983, 95% CI 1.303, 924,798.262) and CD8(+) T cells (P = 0.010, OR = 0.903, 95% CI 0.835, 0.975) were independent risk factors for general type patients to turn to severe type. The area under ROC curve of lactate and CD8(+) T cells was 0.754 (0.581, 0.928) and 0.842 (0.713, 0.970), respectively. The actual observation value was highly consistent with the prediction model value in curve fitting. The established prediction model was verified in 78 COVID-19 patients in the verification set, the area under the ROC curve was 0.906 (0.861, 0.981), and the calibration curve was consistent. CD8(+) T cells, as an independent risk factor, could predict the transition from common covid-19 to severe. Nature Publishing Group UK 2021-01-26 /pmc/articles/PMC7838185/ /pubmed/33500507 http://dx.doi.org/10.1038/s41598-021-81732-4 Text en © The Author(s) 2021 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/.
spellingShingle Article
Liu, Li
Chen, Zhiyong
Du, Yingrong
Gao, Jianpeng
Li, Junyi
Deng, Tiqin
Chen, Chen
Wang, Lin
Yang, Yongrui
Liu, Chunyun
CD8(+) T cells predicted the conversion of common covid-19 to severe
title CD8(+) T cells predicted the conversion of common covid-19 to severe
title_full CD8(+) T cells predicted the conversion of common covid-19 to severe
title_fullStr CD8(+) T cells predicted the conversion of common covid-19 to severe
title_full_unstemmed CD8(+) T cells predicted the conversion of common covid-19 to severe
title_short CD8(+) T cells predicted the conversion of common covid-19 to severe
title_sort cd8(+) t cells predicted the conversion of common covid-19 to severe
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838185/
https://www.ncbi.nlm.nih.gov/pubmed/33500507
http://dx.doi.org/10.1038/s41598-021-81732-4
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