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Research article
Wei Wang
Corresponding Author
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Background
COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding and disease progression, then develop and validate two prognostic discriminant models.
Methods
This study included 125 hospitalized patients with COVID-19. 44 parameters were recorded, including age, gender, underlying comorbidities, epidemic features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. F-test and χ2 test were used for feature selection. All models were developed with 4-fold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models sensitivity and specificity.
Results
69 patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Eleven and six demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding) = -0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) − 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = -0.348–0.099x2 (interval from Jan 1st, 2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short- term exposure to Wuhan) − 0.1646x19 (lymphocyte counts) + 0.0914x20 (neutrophil counts) + 0.1254x21 (neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase). The output ≥ 0 predicted postponed viral shedding or disease progressing to severe/critical state. These two models yielded the maximum AUROC, and faired best in terms of prognostic performance (sensitivity of 73.3%, 75%, and specificity of 78.6%, 75% for prediction of postponed viral shedding and disease severity, respectively).
Conclusion
The two discriminant models could effectively predict the postponed viral shedding and disease severity, and be used as early-warning tools for COVID-19.
Keywords
prognostic discriminant model, postponed viral shedding time, disease progression, COVID-19
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CURRENT STATUS: Under Review
Version 1
Posted 24 Jul, 2020
No community comments so far
Review #2 received
Received 13 Sep, 2020
Editorial decision: Major revision
On 13 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Reviewer #2 agreed
On 26 Aug, 2020
Reviewer #1 agreed
On 20 Aug, 2020
Reviewers invited
Invitations sent on 28 Jul, 2020
First submitted
On 13 Jul, 2020
Editor assigned
On 13 Jul, 2020
Submission checks complete
On 12 Jul, 2020
Editor invited
On 12 Jul, 2020
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PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
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A new preprint design is coming!
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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
Wei Wang
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 24 Jul, 2020
No community comments so far
Review #2 received
Received 13 Sep, 2020
Editorial decision: Major revision
On 13 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Reviewer #2 agreed
On 26 Aug, 2020
Reviewer #1 agreed
On 20 Aug, 2020
Reviewers invited
Invitations sent on 28 Jul, 2020
First submitted
On 13 Jul, 2020
Editor assigned
On 13 Jul, 2020
Submission checks complete
On 12 Jul, 2020
Editor invited
On 12 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
Background
COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding and disease progression, then develop and validate two prognostic discriminant models.
Methods
This study included 125 hospitalized patients with COVID-19. 44 parameters were recorded, including age, gender, underlying comorbidities, epidemic features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. F-test and χ2 test were used for feature selection. All models were developed with 4-fold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models sensitivity and specificity.
Results
69 patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Eleven and six demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding) = -0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) − 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = -0.348–0.099x2 (interval from Jan 1st, 2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short- term exposure to Wuhan) − 0.1646x19 (lymphocyte counts) + 0.0914x20 (neutrophil counts) + 0.1254x21 (neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase). The output ≥ 0 predicted postponed viral shedding or disease progressing to severe/critical state. These two models yielded the maximum AUROC, and faired best in terms of prognostic performance (sensitivity of 73.3%, 75%, and specificity of 78.6%, 75% for prediction of postponed viral shedding and disease severity, respectively).
Conclusion
The two discriminant models could effectively predict the postponed viral shedding and disease severity, and be used as early-warning tools for COVID-19.
Figure 1
Figure 2
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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