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Multicenter Study
. 2020 May 18;15(5):e0233328.
doi: 10.1371/journal.pone.0233328. eCollection 2020.

Development and Validation a Nomogram for Predicting the Risk of Severe COVID-19: A Multi-Center Study in Sichuan, China

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Free PMC article
Multicenter Study

Development and Validation a Nomogram for Predicting the Risk of Severe COVID-19: A Multi-Center Study in Sichuan, China

Yiwu Zhou et al. PLoS One. .
Free PMC article

Abstract

Background: Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19.

Methods: A cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping.

Results: The median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801-0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful.

Conclusion: We established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Selection of demographic and clinical features using the least absolute shrinkage and selection operator (LASSO) logistic regression model.
(a). Selection of optimal parameters (lambda) from the LASSO model using five-fold cross-validation and minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted versus log(lambda). Dotted vertical lines were drawn at the optimal values using the minimum criteria and the 1 standard error of the minimum criteria (1-SE criteria). (b). LASSO coefficient profiles of 24 features. A coefficient profile plot was produced against the log(lambda) sequence. A vertical line was drawn at the value.
Fig 2
Fig 2. Development of a nomogram for predicting severe COVID-19.
The nomogram included body temperature at admission, oxygen saturation, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The nomogram summed the scores for each scale and variable. The total score on each scale indicated the risk of severe COVID-19.
Fig 3
Fig 3
a. Calibration curves of the nomogram for predicting severe COVID-19. Data on predicted and actual disease severity were plotted on the x- and y-axis, respectively. The diagonal dotted line indicates the ideal nomogram, in which actual and predicted probabilities are identical. The solid line indicates the actual nomogram, and a better fit to the dotted line indicates a better calibration. b. Decision curves of the nomogram predicting severe COVID-19. The x-axis represents threshold probabilities and the y-axis measures the net benefit calculated by adding true positives and subtracting false positives. c. Receiver-operating characteristic curve of the nomogram for predicting severe COVID-19.

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Publication types

Supplementary concepts

Grant support

This study was funded by the Emergency Response Project for New Coronavirus of the Science and Technology Department of Sichuan Provincial (Process No. 2020YFS0009, 2020YFS0005) and the Science and Technology Benefit People Project of Chengdu Municipality (Process No. 2016-HM02-00099-SF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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