Abstract:

Objective To explore the efficiency of nomogram based on quantitative CT in prediction of severe corona virus disease 2019（COVID⁃19）. Methods The clinical and chest CT data on 117 COVID⁃19 patients were retrospectively analyzed. All the patients were divided into a mild group（82 patients）and a severe group （35 patients）. Multivariate Logistic regression analysis was performed on the clinical and quantitative CT indexes which had significant differences between the two groups to determine the independent risk factors associated with severe COVID⁃19. Nomogram was constructed for predicting severe COVID⁃19，and then verified by ROC analysis， calibration curve and Hosmer⁃Lemeshow goodness⁃of⁃fit test. Results Multivariate logistic regression showed that age，lymphocyte count⁃to⁃white blood cell ratio，LeV%，and MLeD were independent risk factors for severe COVID⁃ 19. The area under the curve of nomogram was 0.969. The calibration curve showed that the predicted probability was highly concordant with the actual probability. Hosmer⁃Lemeshow goodness⁃of⁃fit test（χ2 = 4.352，P = 0.824） showed that nomogram had higher efficiency in predicting severe COVID⁃19. Conclusions The nomogram based on quantitative CT has better efficiency and application value in predicting severe COVID⁃19. 

Key words:

COVID?19,  , quantitative CT,  , nomogram