Evaluation of Clinical Laboratory Findings with Computed Tomography Segmentation-Volume Analysis Results in COVID-19 Patients

Abstract

Objective: COVID-19 is a disease caused by SARS-COV-2 and early diagnosis and classification of the COVID-19 are critical for the better prognosis. This study aimed to combine laboratory data of COVID-19 patients with Computed Tomography Segmentation-Volume Analysis (CT-SVA). Thus, we hope to contribute to the early diagnosis and classification of the disease. Methods: Patients were divided into two groups according to disease severity as mild/moderate (n=41) and severe/critical (n=42). Some laboratory parameters were recorded and evaluated together with CT-SVA. Results: The results of the study have shown that sodium, C-reactive protein, D-dimer, ferritin, fibrinogen, interleukin 6, procalcitonin, white blood cells, neutrophil, neutrophil-lymphocyte ratio values were significantly higher at first admission in the severe/critical diseased group (p<0.05), while albumin, lymphocyte, and venous blood pH values were significantly lower (p<0.05). CT-SVA results have shown negative correlation with albumin, while having a positive correlation with C-reactive protein, D-dimer, ferritin, fibrinogen, interleukin 6 and procalcitonin. The results of the performed Receiver Operating Characteristics analysis revealed that CT-SVA has a cut-off value of 15.92 with a sensitivity of 87.1% and a specificity of 80.0% in predicting disease severity. Binary logistic regression model has included CT-SVA, D-dimer, ferritin, interleukin 6, and neutrophil-lymphocyte ratio. The model correctly classified 88.1% of cases. CT-SVA, D-dimer, ferritin, interleukin 6, and neutrophil-lymphocyte ratio were detected to be the independent predictors of disease severity. Conclusion: Evaluation of laboratory parameters together with CT-SVA results will help identification of cases with a poor prognosis and accelerate intervention.