tetano
Editor, Senior Moderator
Diagn Interv Imaging
. 2020 Dec 17;S2211-5684(20)30306-5.
doi: 10.1016/j.diii.2020.12.002. Online ahead of print.
COVID-19: A qualitative chest CT model to identify severe form of the disease
Antoine Devie[SUP] 1 [/SUP], Lukshe Kanagaratnam[SUP] 2 [/SUP], Jeanne-Marie Perotin[SUP] 3 [/SUP], Damien Jolly[SUP] 2 [/SUP], Jean-No?l Ravey[SUP] 4 [/SUP], Manel Djelouah[SUP] 5 [/SUP], Christine Hoeffel[SUP] 6 [/SUP]
Affiliations
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: 24-96years). 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.
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: COVID-19; Risk factors; Severe acute respiratory syndrome coronavirus 2; Severity of illness index; Tomography; X-ray computed (CT).
. 2020 Dec 17;S2211-5684(20)30306-5.
doi: 10.1016/j.diii.2020.12.002. Online ahead of print.
COVID-19: A qualitative chest CT model to identify severe form of the disease
Antoine Devie[SUP] 1 [/SUP], Lukshe Kanagaratnam[SUP] 2 [/SUP], Jeanne-Marie Perotin[SUP] 3 [/SUP], Damien Jolly[SUP] 2 [/SUP], Jean-No?l Ravey[SUP] 4 [/SUP], Manel Djelouah[SUP] 5 [/SUP], Christine Hoeffel[SUP] 6 [/SUP]
Affiliations
- PMID: 33419693
- DOI: 10.1016/j.diii.2020.12.002
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: 24-96years). 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.
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: COVID-19; Risk factors; Severe acute respiratory syndrome coronavirus 2; Severity of illness index; Tomography; X-ray computed (CT).