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

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).

Fig. 1

Study flow chart. COVID-19: Coronavirus disease 2019; CT: computed tomography; RT-PCR: reverse transcriptase-polymerase chain reaction.

Fig. 2

Receiver operating characteristic (ROC) analysis of the total CT score taken as a categorical variable with a cutoff value of 15: the area under the curve to identify patients at risk of a severe form of Coronavirus disease 2019 (COVID-19) was 0.672 (95% CI: 0.597–0.748). The cutoff value of 15 yielded 53.4% sensitivity and 81.0% specificity. AUC: area under the curve; CI: confidence interval.

Fig. 3

Graph shows receiver operating characteristic (ROC) curves of the total CT score and the New Early Warning Score 2 (NEWS2) as continuous variables for diagnosing severe Coronavirus disease 2019 (COVID-19) patients. No significant difference in diagnostic performances between the total CT score and the NEWS2 as continuous variables was found (P = 0.327).

Fig. 4

Graph shows receiver operating characteristic (ROC) curves of the qualitative and the semi-quantitative multivariate models for diagnosing severe Coronavirus disease 2019 (COVID-19) patients. No significant difference in diagnostic performances between these two models was found (P = 0.209).