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.
ABSTRACT
OBJECTIVE: Although many studies reported prognostic factors proceeding to severity of COVID-19 patients, in none of the article a prediction scoring model has been proposed. In this article a new prediction tool is presented in combination of Turkish experience during pandemic. MATERIALS AND METHODS: Laboratory and clinical data of 397 over 798 confirmed COVID-19 patients from Gülhane Training and Research Hospital electronic medical record system were included into this retrospective cohort study between the dates of 23 March to 18 May 2020. Patient demographics, peripheral venous blood parameters, symptoms at admission, in hospital mortality data were collected. Non-survivor and survivor patients were compared to find out a prediction scoring model for mortality. RESULTS: There was 34 [8.56% (95% CI:0.06-0.11)] mortality during study period. Mean age of patients was 57.1±16.7 years. Older age, comorbid diseases, symptoms, such as fever, dyspnea, fatigue and gastrointestinal and WBC, neutrophil, lymphocyte count, C-reactive protein, neutrophil-to-lymphocyte ratio of patients in non-survivors were significantly higher. Univariate analysis demonstrated that OR for prognostic nutritional index (PNI) tertile 1 was 18.57 (95% CI: 4.39-78.65, p<0.05) compared to tertile 2. Performance statistics of prediction scoring method showed 98% positive predictive value for criteria 1. CONCLUSIONS: It is crucial to constitute prognostic clinical and laboratory parameters for faster delineation of patients who are prone to worse prognosis. Suggested prediction scoring method may guide healthcare professional to discriminate severe COVID-19 patients and provide prompt intensive therapies which is highly important due to rapid progression leading to mortality.