A Multivariate Model for Predicting the Progress of COVID-19 Using Clinical Data besides Chest CT Scan

Objective. Computed tomography (CT) scan is a method to predict the progression and prognosis of COVID-19. It is not sufficient merely to measure the prognosis of COVID-19 without other clinical methods. The purpose of this study was to investigate the association between the CT scan and clinical laboratory indicators as well as clinical manifestations. Method. A total of 335 patients were enrolled from January 26, 2020, to February 26, 2020, in Shandong province and Huanggang city. Demographic and clinical characteristics, laboratory variables, and the data from the CT scans were collected for analysis. Scatter plot analysis and correlation analysis were used to calculate the relationship between CT evaluation and other indicators. Multivariable linear regression analysis was used to establish a model for diagnostic and prognostic prediction. Age, CRP, LDH, and lymphocyte counts as independent variables were selected to develop a predictive model, and the results from the CT scans to reflect the degree of lung injury were taken as the dependent variable. Result. The median age was 44 years (IQR: 34-56); among them, 188 (56%) were male. Severe patients were older (56 vs. 40, P < 0.001). There were statistically significant differences in lymphocyte counts, platelet counts, C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT), and creatine kinase (CK) between the general patients and severe patients. We found that, without effective antiviral treatment, mild patients had a 6-day interval from symptom onset to CRP elevation, but in severe patients, CRP started to increase from day 2. Lung injury score from a chest CT scan and incidence of acute respiratory distress syndrome (ARDS) were significantly higher in severe patients than in mild patients. Lung injury score from a chest CT scan was closely correlated with CRP (r(s) = 0.704, P < 0.01), and they reflected the severity of the disease. The receiver operating curve (ROC) value of the injury score from the chest CT scan was 0.854 (95% CI: 0.808-0.901), and the area under the curve (AUC) value of CRP was 0.823 (95% CI: 0.769-0.878). Conclusion. The results from CRP and chest CT scans were indicators of the severity of COVID-19. Combining patient age, CRP, LDH, and lymphocyte counts, we developed a model that could help to predict lung injury/function of patients with COVID-19.