Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study

A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profi...

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Autores principales:	Yalan Yu, Tao Liu, Liang Shao, Xinyi Li, Colin K. He, Muhammad Jamal, Yi Luo, Yingying Wang, Yanan Liu, Yufeng Shang, Yunbao Pan, Xinghuan Wang, Fuling Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2020
Materias:	covid-19 serum amyloid a protein disease progression risk factor predictor biomarker Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/aeda50ee6d614fe1a96874fcbbb71bde
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spelling	oai:doaj.org-article:aeda50ee6d614fe1a96874fcbbb71bde2021-11-17T14:21:59ZNovel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study2150-55942150-560810.1080/21505594.2020.1840108https://doaj.org/article/aeda50ee6d614fe1a96874fcbbb71bde2020-12-01T00:00:00Zhttp://dx.doi.org/10.1080/21505594.2020.1840108https://doaj.org/toc/2150-5594https://doaj.org/toc/2150-5608A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profiles of COVID-19 patients and demonstrate their implications for the illness progression of COVID-19. Retrospective analysis of 3,265 confirmed COVID-19 cases hospitalized between 10 January 2020, and 26 March 2020 in three medical centers in Wuhan, China. Patients were diagnosed as COVID-19 and hospitalized in Leishenshan Hospital, Zhongnan Hospital of Wuhan University and The Seventh Hospital of Wuhan, China. Univariable and multivariable logistic regression models were used to determine the possible risk factors for disease progression. Moreover, cutoff values, the sensitivity and specificity of inflammatory parameters for disease progression were determined by MedCalc Version 19.2.0. Age (95%CI, 1.017 to 1.048; P < 0.001), serum amyloid A protein (SAA) (95%CI, 1.216 to 1.396; P < 0.001) and erythrocyte sedimentation rate (ESR) (95%CI, 1.006 to 1.045; P < 0.001) were likely the risk factors for the disease progression. The Area under the curve (AUC) of SAA for the progression of COVID-19 was 0.923, with the best predictive cutoff value of SAA of 12.4 mg/L, with a sensitivity of 83.9% and a specificity of 97.67%. SAA-containing parameters are novel promising ones for predicting disease progression in COVID-19.Yalan YuTao LiuLiang ShaoXinyi LiColin K. HeMuhammad JamalYi LuoYingying WangYanan LiuYufeng ShangYunbao PanXinghuan WangFuling ZhouTaylor & Francis Grouparticlecovid-19serum amyloid a proteindisease progressionrisk factorpredictorbiomarkerInfectious and parasitic diseasesRC109-216ENVirulence, Vol 11, Iss 1, Pp 1569-1581 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 serum amyloid a protein disease progression risk factor predictor biomarker Infectious and parasitic diseases RC109-216
spellingShingle	covid-19 serum amyloid a protein disease progression risk factor predictor biomarker Infectious and parasitic diseases RC109-216 Yalan Yu Tao Liu Liang Shao Xinyi Li Colin K. He Muhammad Jamal Yi Luo Yingying Wang Yanan Liu Yufeng Shang Yunbao Pan Xinghuan Wang Fuling Zhou Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
description	A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profiles of COVID-19 patients and demonstrate their implications for the illness progression of COVID-19. Retrospective analysis of 3,265 confirmed COVID-19 cases hospitalized between 10 January 2020, and 26 March 2020 in three medical centers in Wuhan, China. Patients were diagnosed as COVID-19 and hospitalized in Leishenshan Hospital, Zhongnan Hospital of Wuhan University and The Seventh Hospital of Wuhan, China. Univariable and multivariable logistic regression models were used to determine the possible risk factors for disease progression. Moreover, cutoff values, the sensitivity and specificity of inflammatory parameters for disease progression were determined by MedCalc Version 19.2.0. Age (95%CI, 1.017 to 1.048; P < 0.001), serum amyloid A protein (SAA) (95%CI, 1.216 to 1.396; P < 0.001) and erythrocyte sedimentation rate (ESR) (95%CI, 1.006 to 1.045; P < 0.001) were likely the risk factors for the disease progression. The Area under the curve (AUC) of SAA for the progression of COVID-19 was 0.923, with the best predictive cutoff value of SAA of 12.4 mg/L, with a sensitivity of 83.9% and a specificity of 97.67%. SAA-containing parameters are novel promising ones for predicting disease progression in COVID-19.
format	article
author	Yalan Yu Tao Liu Liang Shao Xinyi Li Colin K. He Muhammad Jamal Yi Luo Yingying Wang Yanan Liu Yufeng Shang Yunbao Pan Xinghuan Wang Fuling Zhou
author_facet	Yalan Yu Tao Liu Liang Shao Xinyi Li Colin K. He Muhammad Jamal Yi Luo Yingying Wang Yanan Liu Yufeng Shang Yunbao Pan Xinghuan Wang Fuling Zhou
author_sort	Yalan Yu
title	Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
title_short	Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
title_full	Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
title_fullStr	Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
title_full_unstemmed	Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study
title_sort	novel biomarkers for the prediction of covid-19 progression a retrospective, multi-center cohort study
publisher	Taylor & Francis Group
publishDate	2020
url	https://doaj.org/article/aeda50ee6d614fe1a96874fcbbb71bde
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Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study

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