Correlation between venous blood gas indices and radiological involvements of COVID-19 patients at first admission to emergency department - Dheir,Hamad (2021)
SUMMARY The purpose of this study was to investigate the relation between venous blood gas and chest computerized tomography findings and the clinical conditions of COVID-19 pneumonia. METHODS: A total of 309 patients admitted to the emergency department and subsequently confirmed COVID-19 cases was examined. Patients with pneumonia symptoms, chest computerized tomography scan, venous blood gas findings, and confirmed COVID-19 on reverse transcription-polymerase chain reaction (PCR) were consecutively enrolled. Multiple linear regression was used to predict computerized tomography and blood gas findings by clinical/laboratory data. RESULTS: The median age of patients was 51 (interquartile range 39–66), and 51.5% were male. The mortality rate at the end of follow-up was 18.8%. With respect to survival status of patients pCO2 and HCO3 levels and total computerized tomography score values were found to be higher in the surviving patients (p<0.001 and p=0.003, respectively), whereas pH and lactate levels were higher in patients who died (p=0.022 and p=0.001, respectively). With logistic regression analysis, total tomography score was found to be significantly effective on mortality (p<0.001). The diffuse and random involvement of the lungs had a significant effect on mortality (p<0.001, 95%CI 3.853–38.769, OR 12.222 and p=0.027; 95%CI 1.155–11.640, OR 3.667, respectively). With linear regression analysis, the effect of pH and lactate results were found to have a positive effect on total tomography score (p=0.003 and p<0.001, respectively), whereas pCO2 was found to have a negative effect (p=0.029). CONCLUSION: There was correlation between venous blood gas indices and radiologic scores in COVID-19 patients. Venous blood gas taken in emergency department can be a fast, applicable, minor-invasive, and complementary test in terms of diagnosing COVID-19 pneumonia and predicting the prognosis of disease.