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Research Article
Predictive Parameters for the Worsening Clinical Course of Mild COVID-19 Pneumonia
Suhyun Kim
Department of Internal Medicine, Seoul Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7802-5967
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Background: This study aimed to determine parameters for worsening oxygenation in mild COVID-19 pneumonia.
Methods: This retrospective cohort study included confirmed COVID-19 pneumonia in a single public hospital in South Korea from January to April 2020. Parameters were compared between the two groups on the basis of clinical course: the desaturation group was defined as those with oxygen saturation ≤ 94% on ambient air, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the nonevent group who were without any respiratory event up to 28 days. The severity and extent of viral pneumonia from an initial single chest CT were calculated using artificial intelligence (AI) algorithms and measured visually by a radiologist.
Results: We included 136 patients with 32 (23.5%) in the desaturation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. In a sex-, age-, and comorbid illness-matched case-control study, ferritin ≥ 280 μg/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), LDH≥ 240 U/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), pneumonia burden (OR 1.010, 95% CI 1.002-1.019; p=0.021), and extent (OR 1.194, 95% CI 1.017-1.401; p=0.030) by AI, and visual severity scores (OR 1.146, 95% CI 1.005-1.307; p=0.042) were the predictive parameters for worsening clinical course with desaturation.
Conclusion: Our study presents initial CT parameters measured by AI or visual severity scoring as well as serum markers of inflammation at admission as the best parameters for predicting worsening oxygenation in the COVID-19 pneumonia cohort. Initial chest CT scans may help clinicians diagnose viral pneumonia and evaluate the prognosis in mild COVID-19.
Keywords
Coronavirus,Viral pneumonia, Mild COVID-19, Computed tomography, artificial intelligence, Oxygenation
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Gang Xie
ORCiDcommented on 12 August, 2020
A warning message on Null Hypothesis Significance Test: “stop using the term ‘statistically significant’ entirely and moving to a world beyond ‘p < 0.05’” : “…, no p-value can reveal the plausibility, presence, truth, or importance of an association or effect. Therefore, a label of statistical significance does not mean or imply that an association or effect is highly probable, real, true, or important. Nor does a label of statistical nonsignificance lead to the association or effect being improbable, absent, false, or unimportant. Yet the dichotomization into ‘significant’ and ‘not significant’ is taken as an imprimatur of authority on these characteristics.” “To be clear, the problem is not that of having only two labels. Results should not be trichotomized, or indeed categorized into any number of groups, based on arbitrary p-value thresholds. Similarly, we need to stop using confidence intervals as another means of dichotomizing (based, on whether a null value falls within the interval). And, to preclude a reappearance of this problem elsewhere, we must not begin arbitrarily categorizing other statistical measures (such as Bayes factors).” Citation from: Ronald L. Wasserstein, Allen L. Schirm & Nicole A. Lazar, Moving to a World Beyond “p<0.05”, The American Statistician(2019), Vol. 73, No. S1, 1-19: Editorial.
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This is a preprint. It has not completed peer review.
Research Article
Predictive Parameters for the Worsening Clinical Course of Mild COVID-19 Pneumonia
Suhyun Kim
Department of Internal Medicine, Seoul Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7802-5967
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 07 Aug, 2020
Community comments: 1
Metrics
Comments: 1
PDF Downloads: ...
HTML Views: ...
Subject Areas
Pulmonology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- This study involved human subjects.
- The author confirmed that all appropriate ethical guidelines for the use of human subjects have been followed, any necessary IRB and/or ethics committee review has been obtained, and information about the IRB/ethics committee is included in the manuscript.
- The author has confirmed that all necessary patient/participant consent or assent has been obtained and the appropriate institutional forms have been archived. If the IRB/ethics committee waived the requirement for patient/participant consent or assent, an explanation for the waiver is included in the text.
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Background: This study aimed to determine parameters for worsening oxygenation in mild COVID-19 pneumonia.
Methods: This retrospective cohort study included confirmed COVID-19 pneumonia in a single public hospital in South Korea from January to April 2020. Parameters were compared between the two groups on the basis of clinical course: the desaturation group was defined as those with oxygen saturation ≤ 94% on ambient air, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the nonevent group who were without any respiratory event up to 28 days. The severity and extent of viral pneumonia from an initial single chest CT were calculated using artificial intelligence (AI) algorithms and measured visually by a radiologist.
Results: We included 136 patients with 32 (23.5%) in the desaturation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. In a sex-, age-, and comorbid illness-matched case-control study, ferritin ≥ 280 μg/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), LDH≥ 240 U/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), pneumonia burden (OR 1.010, 95% CI 1.002-1.019; p=0.021), and extent (OR 1.194, 95% CI 1.017-1.401; p=0.030) by AI, and visual severity scores (OR 1.146, 95% CI 1.005-1.307; p=0.042) were the predictive parameters for worsening clinical course with desaturation.
Conclusion: Our study presents initial CT parameters measured by AI or visual severity scoring as well as serum markers of inflammation at admission as the best parameters for predicting worsening oxygenation in the COVID-19 pneumonia cohort. Initial chest CT scans may help clinicians diagnose viral pneumonia and evaluate the prognosis in mild COVID-19.
Figure 1
Figure 2
Gang Xie
ORCiDcommented on 12 August, 2020
A warning message on Null Hypothesis Significance Test: “stop using the term ‘statistically significant’ entirely and moving to a world beyond ‘p < 0.05’” : “…, no p-value can reveal the plausibility, presence, truth, or importance of an association or effect. Therefore, a label of statistical significance does not mean or imply that an association or effect is highly probable, real, true, or important. Nor does a label of statistical nonsignificance lead to the association or effect being improbable, absent, false, or unimportant. Yet the dichotomization into ‘significant’ and ‘not significant’ is taken as an imprimatur of authority on these characteristics.” “To be clear, the problem is not that of having only two labels. Results should not be trichotomized, or indeed categorized into any number of groups, based on arbitrary p-value thresholds. Similarly, we need to stop using confidence intervals as another means of dichotomizing (based, on whether a null value falls within the interval). And, to preclude a reappearance of this problem elsewhere, we must not begin arbitrarily categorizing other statistical measures (such as Bayes factors).” Citation from: Ronald L. Wasserstein, Allen L. Schirm & Nicole A. Lazar, Moving to a World Beyond “p<0.05”, The American Statistician(2019), Vol. 73, No. S1, 1-19: Editorial.