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Research article
Jinsong Huang
Hangzhou Xixi Hospital
Corresponding Author
Shourong Liu
Hangzhou Xixi Hospital
Corresponding Author
Yimin Zhu
Zhejiang University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19.
Methods: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively.
Results: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values <0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score.
Conclusions: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.
Keywords
COVID-19, severity, assessment model, prediction, web-based assessment system
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This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1revision.docx
Additional file 1: Table S1. Associations of continuous laboratory markers with the severity of COVID-19 in the training set. Table S2. Remaining frequency of the 16 markers in 1,000 LASSO regression models. Table S3. Associations of the selected eight markers with the severity of COVID-19 in the external validation set (DOCX).
- Additionalfile1revision.docx
Additional file 1: Table S1. Associations of continuous laboratory markers with the severity of COVID-19 in the training set. Table S2. Remaining frequency of the 16 markers in 1,000 LASSO regression models. Table S3. Associations of the selected eight markers with the severity of COVID-19 in the external validation set (DOCX).
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See the published version of this preprint at BMC Infectious Diseases.
This is a preprint, 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
Jinsong Huang
Hangzhou Xixi Hospital
Corresponding Author
Shourong Liu
Hangzhou Xixi Hospital
Corresponding Author
Yimin Zhu
Zhejiang 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: Published
View the published article at BMC Infectious Diseases.
Version 2
Posted 16 Dec, 2020
No community comments so far
Editor assigned
On 02 Dec, 2020
Submission checks complete
On 02 Dec, 2020
Editor invited
On 02 Dec, 2020
No comments provided
Review #2 received
Received 21 Nov, 2020
Editorial decision: Minor revision
On 21 Nov, 2020
Reviewer #2 agreed
On 29 Oct, 2020
Review #1 received
Received 10 Oct, 2020
Reviewer #1 agreed
On 30 Aug, 2020
Reviewers invited
Invitations sent on 27 Aug, 2020
Editor assigned
On 24 Aug, 2020
Submission checks complete
On 23 Aug, 2020
Editor invited
On 23 Aug, 2020
First submitted
On 22 Jul, 2020
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
View the published article at BMC Infectious Diseases.
Background: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19.
Methods: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively.
Results: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values <0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score.
Conclusions: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1revision.docx
Additional file 1: Table S1. Associations of continuous laboratory markers with the severity of COVID-19 in the training set. Table S2. Remaining frequency of the 16 markers in 1,000 LASSO regression models. Table S3. Associations of the selected eight markers with the severity of COVID-19 in the external validation set (DOCX).
- Additionalfile1revision.docx
Additional file 1: Table S1. Associations of continuous laboratory markers with the severity of COVID-19 in the training set. Table S2. Remaining frequency of the 16 markers in 1,000 LASSO regression models. Table S3. Associations of the selected eight markers with the severity of COVID-19 in the external validation set (DOCX).
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