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Research Article
Prediction for the Negative Conversion Probability of Nucleic Acid Testing in Patients with Nonsevere COVID-19 Pneumonia: A Model Based on Retrospective Cohort Study
Ping Han
Sichuan University West China Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9710-0037
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: We aimed to screen clinical independent predictive factors for negative conversion of nucleic acid testing and established a predictive nomogram, so as to relieve patients' anxiety and reduce unnecessary repeated nucleic acid testing.
Methods: All 70 consecutive patients with nonsevere COVID-19 pneumonia were admitted to the Fangcang shelter hospital in Wuhan from February 12th to March 8th, 2020. We used univariate Kaplan-Meier analysis and univariate and multivariate Cox regression to identify independent predictive factors and refit the predictive model. Area under ROC (AUR), Brier scores and calibration plots were used to assess the performance.
Results: diabetes mellitus, gender and lymphocyte were deemed independent predictive factors and were incorporated into a Cox proportional hazards model. The AUR and Brier scores of the predictive model at 14 days were 0.694 [0.472; 0.890] and 0.163 [0.109; 0.219] in the internal validation set, respectively. Similarly, the AUR and Brier scores at 21 days were 0.779 [0.505; 0.957] and 0.105 [0.042; 0.175] in the internal validation set.
Conclusions: By using the predictive nomogram, the clinicians could inform patients with nonsevere COVID-19 regarding a certain time to possible negative conversion, which would relieve the patients’ anxiety and reduce repeated testing.
Keywords
coronavirus, COVID-19, prediction, nomogram
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This is a preprint. It has not completed peer review.
Research Article
Prediction for the Negative Conversion Probability of Nucleic Acid Testing in Patients with Nonsevere COVID-19 Pneumonia: A Model Based on Retrospective Cohort Study
Ping Han
Sichuan University West China Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9710-0037
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 19 May, 2020
No community comments so far
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: We aimed to screen clinical independent predictive factors for negative conversion of nucleic acid testing and established a predictive nomogram, so as to relieve patients' anxiety and reduce unnecessary repeated nucleic acid testing.
Methods: All 70 consecutive patients with nonsevere COVID-19 pneumonia were admitted to the Fangcang shelter hospital in Wuhan from February 12th to March 8th, 2020. We used univariate Kaplan-Meier analysis and univariate and multivariate Cox regression to identify independent predictive factors and refit the predictive model. Area under ROC (AUR), Brier scores and calibration plots were used to assess the performance.
Results: diabetes mellitus, gender and lymphocyte were deemed independent predictive factors and were incorporated into a Cox proportional hazards model. The AUR and Brier scores of the predictive model at 14 days were 0.694 [0.472; 0.890] and 0.163 [0.109; 0.219] in the internal validation set, respectively. Similarly, the AUR and Brier scores at 21 days were 0.779 [0.505; 0.957] and 0.105 [0.042; 0.175] in the internal validation set.
Conclusions: By using the predictive nomogram, the clinicians could inform patients with nonsevere COVID-19 regarding a certain time to possible negative conversion, which would relieve the patients’ anxiety and reduce repeated testing.
Figure 1
Figure 2
Figure 3
Figure 4