ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) was recognized by the World Health Organization (WHO) as a global pandemic on March 11, 2020. Since then, researchers worldwide have focused their attention on identifying effective treatments and developing vaccines to combat this disease. Aim: To report the effectiveness of the drugs employed in the COVID-19 treatment protocols based on data from clinical trial studies conducted from the beginning of the pandemic until December 10, 2020. Methods: Following the PRISMA guidelines, we conducted an advanced search in several electronic databases. A total of 13553 studies was screened by two people simultaneously and separately based on the article title, and full-text. The quality of the studies was evaluated using the Cochrane criteria. Results: Of the 13553 studies identified, 50 clinical trials were included in this systematic review. Of these, three studies explored the use of remdesivir, nine studies the use of hydroxychloroquine, five studies the use of lopinavir/ritonavir, six studies the use of favipiravir, one study the use of tocilizumab, two studies the use of interferon beta-1a and two studies the use of umifenovir.
ABSTRACT
Introduction: Few studies have been conducted to construct a reliable predictive model for the differential diagnosis of severe and non-severe Coronavirus disease-2019 (COVID-19) in the early stages of the disease. This study aimed to compare the accuracy of linear discriminate analysis (LDA) and binary logistic regression (BLR), as two empirical correlations, in predicting COVID-19 severity using single laboratory data and calculated indexes such as the neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Materials and Methods: We investigated 109 patients with confirmed COVID-19 pneumonia. Epidemiological, demographic, clinical, laboratory, and outcome data were obtained, and the patients were classified into two groups: mild group (42 patients) and severe group (67 patients). Results: A comparison of the clinical data in the severe and non-severe groups showed significant differences in SpO(2) and respiratory rate. In addition, significant difference in NLR, SII, white blood cell count, neutrophil count, mean corpuscular volume and mean corpuscular hemoglobin, lymphocyte count, erythrocyte sedimentation rate, lactate dehydrogenase, and blood urea nitrogen was found between both groups. Moreover, there was a small difference between the LDA and LR models, and LDA was more appropriate for a smaller sample size. Conclusion: Our predictive models could help clinicians to identify patients at risk of severe COVID-19 Such prediction can be performed by a simple blood test. LDA and BLR can be used to effectively classify patients with severe and non-severe COVID-19, even with violation of the normality assumption.