COVID-19: A qualitative chest CT model to identify severe form of the disease

•Chest CT helps identify patients with severe COVID-19 using only three qualitative features.•A qualitative model based on three qualitative variables can avoid calculating semi-quantitative total CT score.•New Early Warning Score 2 is comparable to the CT score for identification of severe forms of...

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Bibliographic Details
Published in:Diagnostic and interventional imaging 2021-02, Vol.102 (2), p.77-84
Main Authors: Devie, Antoine, Kanagaratnam, Lukshe, Perotin, Jeanne-Marie, Jolly, Damien, Ravey, Jean-Noël, Djelouah, Manel, Hoeffel, Christine
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Bioengineering
Bronchiectasis - diagnostic imaging
Computer Simulation
COVID-19
COVID-19 - diagnostic imaging
Female
Humans
Imaging
Life Sciences
Lung - diagnostic imaging
Male
Middle Aged
Multivariate Analysis
Original
Risk Factors
ROC Curve
Severe acute respiratory syndrome coronavirus 2
Severity of Illness Index
Tomography
Tomography, X-Ray Computed
X-ray computed (CT)
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Summary:•Chest CT helps identify patients with severe COVID-19 using only three qualitative features.•A qualitative model based on three qualitative variables can avoid calculating semi-quantitative total CT score.•New Early Warning Score 2 is comparable to the CT score for identification of severe forms of COVID-19. The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range: 24–96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. Central involvement of lung parenchyma (P
ISSN:2211-5684
2211-5684
DOI:10.1016/j.diii.2020.12.002