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Clinical, radiological and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients
  • +8
  • Jin-jin Zhang,
  • Yi-yuan Cao,
  • Ge TanOrcid,
  • Xiang Dong,
  • Bin-chen Wang,
  • Jun Lin,
  • You-qin Yan,
  • Guanghui Liu,
  • Mubeccel Akdis,
  • Cezmi Akdis,
  • Ya-dong Gao
Jin-jin Zhang
Wuhan University Zhongnan Hospital
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Yi-yuan Cao
Wuhan University Zhongnan Hospital Department of Radiology
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Ge Tan
Orcid
Swiss Institute of Allergy and Asthma Research
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Xiang Dong
Wuhan University Zhongnan Hospital
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Bin-chen Wang
Wuhan University Zhongnan Hospital
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Jun Lin
Wuhan University Zhongnan Hospital
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You-qin Yan
No. 7 Hospital of Wuhan
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Guanghui Liu
Wuhan University Zhongnan Hospital
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Mubeccel Akdis
University of Z├╝rich
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Cezmi Akdis
University of Zurich
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Ya-dong Gao
Wuhan University Zhongnan Hospital
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Peer review status:ACCEPTED

08 Jun 2020Submitted to Allergy
08 Jun 2020Submission Checks Completed
08 Jun 2020Assigned to Editor
09 Jun 2020Reviewer(s) Assigned
24 Jun 2020Review(s) Completed, Editorial Evaluation Pending
25 Jun 2020Editorial Decision: Revise Minor
07 Jul 20201st Revision Received
07 Jul 2020Submission Checks Completed
07 Jul 2020Assigned to Editor
07 Jul 2020Reviewer(s) Assigned
08 Jul 2020Review(s) Completed, Editorial Evaluation Pending
08 Jul 2020Editorial Decision: Accept

Abstract

Background Currently, the coronavirus disease 2019 (COVID-19) has become pandemic globally. 10-20% of the cases are severe and more than 397,000 deaths have occurred. The risk factors for the mortality of critically ill COVID-19 patients remain to be elucidated. Conclusions Survived severe and non-survived COVID-19 patients had distinct clinical and laboratory characteristics, which were separated by principle component analysis. Logistic regression revealed several risk factors such as elder age, greater affected lobe numbers and higher level of serum CRP for the mortality of severe COVID-19 patients. Longitudinal changes of laboratory findings indicate the advancement of the disease and may be helpful in predicting the progression of severe patients.