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...

Full description

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Virulence
Subjects:
Online Access:http://dx.doi.org/10.1080/21505594.2020.1840108
id doaj-aeda50ee6d614fe1a96874fcbbb71bde
record_format Article
spelling 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
work_keys_str_mv AT yalanyu novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT taoliu novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT liangshao novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT xinyili novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT colinkhe novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT muhammadjamal novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT yiluo novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT yingyingwang novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT yananliu novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT yufengshang novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT yunbaopan novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT xinghuanwang novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
AT fulingzhou novelbiomarkersforthepredictionofcovid19progressionaretrospectivemulticentercohortstudy
_version_ 1714944405106851840