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: 24–96 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 Conclusion – We have developed a new qualitative CT-based multivariate model (NEWS2) that provides independent risk factors associated with severe COVID-19 with performances similar to those of the total semi-quantitative CT score.