Aim: COVID-19 is a pandemic that causes high morbidity and mortality, especially in severe patients. In this study, we aimed to search and explain the relationship between biochemical markers, which are more common, easily available and applicable to diagnose and to stage the disease. Materials & methods: In this study, 609 patients were evaluated retrospectively. 11 biochemical parameters were included in analysis to explain the relationship with severity of disease. Results: Nearly, all the parameters that have been evaluated in this study were statistically valuable as a predictive parameter for severe disease. Areas under the curve of blood urea nitrogen (BUN)/albumin ratio (BAR), CALL score and lymphocyte/C-reactive protein ratio were 0.795, 0.778 and 0.770. The BUN/BAR and neutrophil/albumin ratios provide important prognostic information for decision-making in severe patients with COVID-19. Conclusion: High BUN/BAR and neutrophil/albumin ratios may be a better predictor of severity COVID-19 than other routinely used parameters in admission.

In this study, we compare the predictive value of clinical scoring systems that are already in use in patients with Coronavirus disease 2019 (COVID-19), including the Brescia-COVID Respiratory Severity Scale (BCRSS), Quick SOFA (qSOFA), Sequential Organ Failure Assessment (SOFA), Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension, and Age (MuLBSTA) and scoring system for reactive hemophagocytic syndrome (HScore), for determining the severity of the disease. Our aim in this study is to determine which scoring system is most useful in determining disease severity and to guide clinicians. We classified the patients into two groups according to the stage of the disease (severe and non-severe) and adopted interim guidance of the World Health Organization. Severe cases were divided into a group of surviving patients and a deceased group according to the prognosis. According to admission values, the BCRSS, qSOFA, SOFA, MuLBSTA, and HScore were evaluated at admission using the worst parameters available in the first 24 h. Of the 417 patients included in our study, 46 (11%) were in the severe group, while 371 (89%) were in the non-severe group. Of these 417 patients, 230 (55.2%) were men. The median (IQR) age of all patients was 44 (25) years. In multivariate logistic regression analyses, BRCSS in the highest tertile (HR 6.1, 95% CI 2.105–17.674, p = 0.001) was determined as an independent predictor of severe disease in cases of COVID-19. In multivariate analyses, qSOFA was also found to be an independent predictor of severe COVID-19 (HR 4.757, 95% CI 1.438–15.730, p = 0.011). The area under the curve (AUC) of the BRCSS, qSOFA, SOFA, MuLBSTA, and HScore was 0.977, 0.961, 0.958, 0.860, and 0.698, respectively. Calculation of the BRCSS and qSOFA at the time of hospital admission can predict critical clinical outcomes in patients with COVID-19, and their predictive value is superior to that of HScore, MuLBSTA, and SOFA. Our prediction is that early interventions for high-risk patients, with early identification of high-risk group using BRCSS and qSOFA, may improve clinical outcomes in COVID-19.

IntroductionCoronavirus 2019 disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pandemic infectious disease that causes morbidity and mortality. To date there are no proven diagnostic or prognostic parameters, but clinicians can use predictive parameters, such as the leukocyte and lymphocyte counts, C-reactive protein (CRP), D-dimer, and ferritin levels, and radiological imaging [1-3]. In the inflammation process of COVID-19, CRP, platelet, ferritin, and leukocyte values may increase, while albumin and lymphocyte values may decrease [4][5][6].White blood cells (WBCs), including neutrophils, lymphocytes, monocytes, have been used as inflammatory Background/aim: Coronavirus 2019 disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pandemic infectious disease that causes morbidity and mortality. As a result of high mortality rate among the severe COVID-19 patients, the early detection of the disease stage and early effective interventions are very important in reducing mortality. Hence, it is important to differentiate severe and nonsevere cases from each other. To date, there are no proven diagnostic or prognostic parameters that can be used in this manner. Due to the expensive and not easily accessible tests that are performed for COVID-19, researchers are investigating some parameters that can be easily used. In some recent studies, hematological parameters have been evaluated to see if they can be used as predictive parameters. Materials and methods:In the current study, almost all hematological parameters were used, including the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, monocyte/lymphocyte ratio, mean platelet volume to lymphocyte ratio, mean platelet volume to platelet ratio, plateletcrit, and D-dimer/fibrinogen ratio, neutrophil/lymphocyte/platelet scoring system, and systemic immune-inflammation index. A total of 750 patients, who were admitted to Ankara City Hospital due to COVID-19, were evaluated in this study. The patients were classified into 2 groups according to their diagnosis (confirmed or probable) and into 2 groups according to the stage of the disease (nonsevere or severe). Results:The values of the combinations of inflammatory markers and other hematological parameters in all of the patients with severe COVID-19 were calculated, and the predicted values of these parameters were compared. According to results of the study, nearly all of the hematological parameters could be used as potential diagnostic biomarkers for subsequent analysis, because the area under the curve (AUC) was higher than 0.50, especially for the DFR and NLR, which had the highest AUC among the parameters. Conclusion:Our findings indicate that, the parameters those enhanced from complete blood count, which is a simple laboratory test, can help to identify and classify COVID-19 patients into non-severe to severe groups.

IntroductionIn this study, we compare the predictive value of clinical scoring systems that are already in use in patients with COVID-19, including the BCRSS, qSOFA, SOFA, MuLBSTA and HScore, for determining the severity of the disease. Our aim in this study is to determine which scoring system is most useful in determining disease severity and to guide clinicians.Materials and MethodsWe classified the patients into two groups according to the stage of the disease (severe and non-severe) by using the slightly modified and adopted interim guidance of the World Health Organization. Severe cases were divided into a group of surviving patients and a deceased group according to the prognosis. According to admission values, the BCRSS, qSOFA, SOFA, MuLBSTA, and HScore were evaluated at admission using the worst parameters available in the first 24 hours.ResultsOf the 417 patients included in our study, 46 (11%) were in the severe group, while 371 (89%) were in the non-severe group. Of these 417 patients, 230 (55.2%) were men. The median (IQR) age of all patients was 44 (25) years. In multivariate logistic regression analyses, BRCSS in the highest tertile (HR: 6.1, 95% CI: 2.105–17.674, p = 0.001) was determined as an independent predictor of severe disease in cases of COVID-19. In multivariate analyses, qSOFA was also found to be an independent predictor of severe COVID-19 (HR: 4.757, 95% CI: 1.438–15.730, p = 0.011). The area under the curve (AUC) of the BRCSS, qSOFA, SOFA, MuLBSTA, and HScore was 0.977, 0.961, 0.958, 0.860, and 0.698, respectively.ConclusionCalculation of the BRCSS and qSOFA at the time of hospital admission can predict critical clinical outcomes in patients with COVID-19, and their predictive value is superior to that of HScore, MuLBSTA, and SOFA. With early identification of the high-risk group using BRCSS and qSOFA, early interventions for high-risk patients can improve clinical outcomes in COVID-19.