Coronavirus, the cause of severe acute respiratory syndrome, has rapidly affected a large number of people in all the world. In regard to a number of deaths and serious consequences of disease, it is so remarkable to early diagnosis of patients and timely treatment (Li et al., 2020b). One of the most important signs in these patients is to assess the chest CT scan that indicated imaging signs related to disease advancement, including increase in GGO, interstitial septal thickening and consolidative opacities (Salehi et al., 2020).
In this retrospective study, the chest CT features of 1078 patients with COVID-19 in critical and non-critical cases were reviewed. The liner opacities, pure GGO, mixed GGO with consolidation, and mixed GGO with crazy-paving pattern have been the most frequent types of lesions with involving bilateral and multifocal distribution. The DT model was applied to predict the critical or non-critical situation of new cases. The total opacity score, number of lung lobes involvement and presence diffuse opacity have been regarded noticeable variables by data mining. In the study, the total opacity score has been considered as an important variable. If the variable is lower than 7.5, the next essential variable will be age. As the total opacity score is more than 7.5, lesion type improvement is 0.011 and also lesion type is GGO as well as consolidation, the occurrence of the critical condition will be equal to 82.6. It is worth mentioning, when the total opacity score is less than 7.5 and age of patient is less than 62.5, it is predicted that the percentage of non-critical status of patient will be 98.4.
The age difference between the two groups was statistically significant and the mean age in non- critical patients was lower than the critical group (P < 0.001). The results of our study are inconsistent with a study of the two groups, and the time from symptom onset to diagnosis and treatment was less than 3 days and more than 3 days (P = 0.76). However, gender was considered as a non-significant variable in both studies (P > 0.05), the conflict of the results can be that the sample size of that study was too small (n = 25) (Kobayashi et al., 2013). In a study by Zhou et al. patients were divided into two groups, patients with COVID-19 in the early stage (n = 34) and in progressive stage (n = 28) and the reults showed that there was no significant difference in age and gender (Zhou et al., 2020). Moreover, a study by Shen et al. revealed that the age and gender was not significant difference between two groups of confirmed COVID-19 as severe and non-severe patients (Shen et al., 2020). In a study by Liu et al. cccording to the diagnosis and treatment protocol, patients were divided into two groups: recovery or stabilization (n = 67) and progress (n = 11), and the results of the study were consistent with our study. It means that age was considered significant, but gender was not significant (Liu et al., 2020b).
In both groups of our study, the common types of lesions were mixed GGO with consolidation, mixed GGO with crazy-paving pattern, liner opacities and pure GG. The frequency of pure consolidation and mixed GGO with consolidation lesions showed a significant difference between the groups, these types were more common in critical patients than in non-critical patients, which it means that the virus diffuses into the respiratory epithelium can cause necrotizing bronchitis and diffuse alveolar damage. Also, in critical patients reveled more intralesional traction bronchiectasis and pleural effusion lesions than in the non-critical patients. These extrapulmonary lesions indicate the occurrence of severe inflammation in critical group. The results of our study were consistent with other chest CT studies, similarly we observed the frequent specific signs in critical patients than in the non-critical patients (53.8% vs. 32.1%, P < 0.001) (Franquet, 2011, Koo et al., 2018). Although, the reversed halo sign and liner opacities were more frequent in non-critical patients, no significant difference was observed between two groups (P > 0.05).
According to the DT model, the total opacity score from the critical group was the fundamental variable for distinguishing the critical group from the non-critical group and its accuracy, sensitivity and specificity was 93.3%, 72.8% and 97.1%, respectively. Our findings were consistent with previous studies that reported the sensitivity and specificity of CT images for the diagnosis of lung lesions from 80–90% as well as 82.8–96% (Li et al., 2020a, Li.L et al., 2020, Liu et al., 2020a).
It is worth to mention the sample size of the study was very large. The first limitation of the study was that the time of chest CT examination and the onest symptom were not simultaneous and therefore it was difficult to summarize the features of CT scan that could be shown during the course of the disease.