World Summit on COVID-19 (Part V)
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Accepted Abstracts

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

Xing Han*
Qingdao University, China.

Citation: Han X (2022) A Multivariate Model for Predicting the Progress of COVID-19 Using Clinical Data besides Chest CT Scan. SciTech Central COVID-19.

Received: January 30, 2022         Accepted: February 03, 2022         Published: February 03, 2022


Objective: Computed tomography (CT) scan is a method to predict the progression and prognosis of COVID-19. However, the use of CT scan might be limited to some extent.The purpose of this study was to investigate the association between 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 CT scan 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.
Result: The median age was 44 (IQR 34-56), 188 (56%) were male among. Severe patients were older(56 vs 40, P < 0.001).Laboratory examination after admission showed that lymphocyte counts, platelet counts, C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT) and creatine kinase (CK)were significantly different in patients with severe illness. 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 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 chest CT scan was closely correlated with CRP (rs=0.704, P<0.01). In addition, these indicators reflected the severity of the disease. The receiver operating curve (ROC)value of injury score form chest CT scan was 0.854 [95% CI: 0.808-0.901], and the area under curve(AUC) value of CRP was 0.823 [95% CI: 0.769-0.878]. Finally, age, CRP, LDHand lymphocyte counts as independent variables were selected to develop predictive model. And the results from CT scan to reflect the degree of lung injury were taken as the dependent variable.
Conclusion: Combining patient age, CRP, LDHand lymphocyte counts, we developed a model that could help to predict lung injury/function of patients with COVID-19.