The comparison of COVID-19 vs seasonal influenza in children.

BACKGROUND: Influenza in children has been well described, whereas there has been a paucity of pediatric data regarding COVID-19. It is crucial for clinicians to differentiate cases of COVID-19 from cases of influenza because of the upcoming influenza season in the new pandemic era. METHODS: This retrospective study included pediatric patients who were diagnosed with laboratory-confirmed COVID-19 between March and September 2020, or seasonal influenza between October 2019 and March 2020. RESULTS: A total of 315 children were included in this study; 151 were diagnosed with influenza and 164 had confirmed COVID-19. The median age of patients with COVID-19 was 10 years (interquartile range [IQR]: 3-15 years), whereas the median age of patients with influenza was 4 years (IQR: 1-6 years) (p = 0.001). In the COVID-19 group, 6.3% of patients had underlying diseases, the most frequent being neurological conditions (3%). In the influenza group, 20.9% of patients had an underlying disease, the most frequent being asthma (14.5%). Fever (odds ratio [OR]: 20.476; 95% confidence interval [CI]: 2.438-171.995; p = 0.005), dyspnea/tachypnea (OR 13.950; 95% CI: 2.607-74.634; p = 0.002), and increased C-reactive protein (CRP) (OR: 7.650; 95% CI: 2.094-27.955; p = 0.002) were main predictors of influenza diagnosis in comparison to COVID-19. Lymphopenia was detected in 43.2% of patients with influenza and 19.9% of patients with COVID-19 (p = 0.001). CONCLUSIONS: The accurate differentiation between "influenza or COVID-19" seems possible by evaluating a combination of factors including cough, fever, vomiting, leucopenia, lymphopenia, pneumonia, in pediatric patients with high CRP as well as age.

Compatibility levels between blood gas analysis and central laboratory hemoglobin and electrolyte tests in pediatric patients: A single-center experience.

INTRODUCTION: We aimed to evaluate the interchangeability of sodium, potassium, hemoglobin, and hematocrit measurement between the blood gas analyzers and laboratory automatic analyzers results. METHODS: This was a retrospective cross-sectional study. The results of 1927 paired samples analyzed simultaneously with the blood gas analyzer and the laboratory automatic analyzer were compared. The Bland-Altman and Cohen's kappa statistic detected the agreement between the two analyses. RESULTS: The limits of agreement (±1.96 standard deviation of the mean difference) were -11.1 to 20.3 for sodium, -1.9 to 0.5 for potassium, -16.1 to 12.9 for hematocrit, and -5.0 to 4.0 for hemoglobin. Agreement between the two analyses was not acceptable within the defined clinically acceptable limits. In addition, none of the kappa values were higher than 0.60, which highlights the lack of agreement between the two analyzers. CONCLUSION: The blood gas analyzers and laboratory automatic analyzers results cannot be used interchangeably.

The association of vitamin D deficiency with hemogram-derived inflammatory biomarkers in children.

BACKGROUND AND AIMS: One of the extraosseous effects of vitamin D is that it is a potent modulator of inflammatory processes. Many studies have demonstrated the inverse association between vitamin D and inflammation. Therefore, we hypothesize that vitamin D deficiency may affect the inflammatory markers derived from hemogram parameters [neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), platelet distribution width (PDW), red blood cell distribution width (RDW)] in healthy children. METHODS AND RESULTS: We conducted a retrospective study on healthy children. From 2015 to 2020, 16,321 children with simultaneous vitamin D and hemogram measurements were identified from electronic records. Participants were divided into 2 groups according to whether they had vitamin D deficiency or not. The relationship between vitamin D status and the levels of inflammatory markers was analyzed. All inflammatory markers showed statistically significant differences between vitamin D status (p < 0.001 for all). Vitamin D levels were significantly negatively correlated with NLR (r = -0.285), PLR (r = -0.257), PDW (r = -0.181), and positively correlated with LMR (r = 0.218), and RDW (r = 0.057). In logistic regression analysis, age (OR = 1.15, 95% CI: 1.14-1.16), gender (OR = 1.66, 95% CI: 1.54-1.78), LMR (OR = 0.96, 95% CI: 0.95-0.98), PLR (OR = 1.003, 95% CI: 1.001-1.004), and RDW (OR = 1.10, 95%CI: 1.07-1.13) were found to be independent predictors for vitamin D deficiency. CONCLUSIONS: Statistically significant differences were detected between vitamin D status and inflammatory parameters. However, the difference between the median values of vitamin D groups was very small and the degree of correlation was very weak. Therefore, the clinical significance of the difference should be questioned.