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Research Article
Death Risk Analysis for Patients With Severe COVID-19 Pneumonia
Yonggang Li
First Affiliated Hospital of Soochow University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about the death predicting of severe COVID-19 is not clear.
Methods: 151 in-patients from January 23th to March 8th 2020 were divided into severe and critically severe group, as well as survival and death group. The analysis of differences of clinical and imaging data were performed between groups. The logistic regression analysis of factors associated with death in COVID-19 were conducted, and the prediction model of death risk was developed.
Results: Many clinical and imaging indices were significantly different between groups, including the age, the epidemic history, the past medical history, the duration of symptoms prior to admission, blood routine, inflammatory related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportion of patients in imaging stage III and comprehensive CT scores was increased significantly in death group. The area under receiver operating characteristic curve of the prediction model was 0.9593.
Conclusions: The clinical and imaging data reflect the severity of COVID-19 pneumonia. The prediction model of death risk might be a promising method to help clinicians to quickly identify and screen potential individuals who had a high-risk of death.
Keywords
COVID-19, Pneumonia, Fatal Outcome
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This preprint is under consideration at BMC Infectious Diseases. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Death Risk Analysis for Patients With Severe COVID-19 Pneumonia
Yonggang Li
First Affiliated Hospital of Soochow University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Jan, 2021
No community comments so far
Editor invited
On 19 Jan, 2021
Submission checks complete
On 19 Jan, 2021
First submitted
On 06 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about the death predicting of severe COVID-19 is not clear.
Methods: 151 in-patients from January 23th to March 8th 2020 were divided into severe and critically severe group, as well as survival and death group. The analysis of differences of clinical and imaging data were performed between groups. The logistic regression analysis of factors associated with death in COVID-19 were conducted, and the prediction model of death risk was developed.
Results: Many clinical and imaging indices were significantly different between groups, including the age, the epidemic history, the past medical history, the duration of symptoms prior to admission, blood routine, inflammatory related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportion of patients in imaging stage III and comprehensive CT scores was increased significantly in death group. The area under receiver operating characteristic curve of the prediction model was 0.9593.
Conclusions: The clinical and imaging data reflect the severity of COVID-19 pneumonia. The prediction model of death risk might be a promising method to help clinicians to quickly identify and screen potential individuals who had a high-risk of death.
Figure 1
Figure 2
Figure 3
Figure 4