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

https://doi.org/10.1016/j.diii.2020.12.002Get rights and content
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Highlights

  • 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.

Abstract

Purpose

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.

Materials and methods

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: 24–96 years). 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.

Results

Central involvement of lung parenchyma (P < 0.001), area of consolidation (P < 0.008), air bronchogram sign (P < 0.001), bronchiectasis (P < 0.001), traction bronchiectasis (P < 0.011), pleural effusion (P < 0.026), large involvement of either one of the upper lobes or of the middle lobe (P < 0.001) and total CT score  15 (P < 0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR) = 2.473], central involvement [OR = 2.760], pleural effusion [OR = 2.699]) and the semi-quantitative model (total CT score  15 [OR = 3.342], central involvement [OR = 2.344], pleural effusion [OR = 2.754]) with AUC of 0.722 (95% CI: 0.638–0.806) vs. 0.739 (95% CI: 0.656–0.823), respectively (P = 0.209).

Conclusion

We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.

Keywords

Severe acute respiratory syndrome coronavirus 2
Tomography
X-ray computed (CT)
COVID-19
Risk factors
Severity of illness index

Abbreviations

AUC
area under the curve
COPD
chronic obstructive pulmonary disease
COVID-19
Coronavirus disease 2019
CT
computed tomography
EWS
Early Warning Score
GGO
ground glass opacity
HU
Hounsfield units
ICU
intensive care unit
LUL
left upper lobe
ML
middle lobe
NEWS2
New Early Warning Score 2
OR
odd ratio
ROC
receiver operating characteristic
RT-PCR
reverse transcriptase-polymerase chain reaction
RUL
right upper lobe
SARS-CoV-2
severe acute respiratory syndrome coronavirus 2
SD
standard deviation

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