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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doaj-aeda50ee6d614fe1a96874fcbbb71bde2021-01-15T14:09:08ZengTaylor & Francis GroupVirulence2150-55942150-56082020-01-011111569158110.1080/21505594.2020.18401081840108Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort studyYalan Yu0Tao Liu1Liang Shao2Xinyi Li3Colin K. He4Muhammad Jamal5Yi Luo6Yingying Wang7Yanan Liu8Yufeng Shang9Yunbao Pan10Xinghuan Wang11Fuling Zhou12Zhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityStego Tech LLCWuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityZhongnan Hospital of Wuhan UniversityA 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.http://dx.doi.org/10.1080/21505594.2020.1840108covid-19serum amyloid a proteindisease progressionrisk factorpredictorbiomarker |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
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 |
spellingShingle |
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 Virulence covid-19 serum amyloid a protein disease progression risk factor predictor biomarker |
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 |
series |
Virulence |
issn |
2150-5594 2150-5608 |
publishDate |
2020-01-01 |
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. |
topic |
covid-19 serum amyloid a protein disease progression risk factor predictor biomarker |
url |
http://dx.doi.org/10.1080/21505594.2020.1840108 |
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