Statistical analyses
The data was analyzed using SPSS 22.0 software (SPSS, Inc., Chicago, IL, USA). Continuous variables with normal distribution were presented as mean ± standard deviation and median [25th–75th percentiles, interquartile range (IQR)] for non-normal variables. Kolmogorov– Smirnov test was used to analyze the distribution of variables and a Levene test to assess the equality of variances. An unpaired Student’s t-test or a Mann–Whitney U test was used to compare the two groups. Categorical data were expressed as numbers and percentages and compared by chi-square test or Fisher’s exact test as appropriate. We compared the demographic and clinical features between subjects that showed a difference between SpO2 and SaO2 ≤ 4% (acceptable difference) or >4% (large difference). This cut-off value was chosen due to a potential error of 3–4% between SpO2 and SaO2 according to the previous data 8-10. The relationships between age, gender and comorbid diseases and laboratory data with large difference between SpO2 and SaO2 were analyzed using binary logistic regression analyses. We used a receiver operating characteristic curve analysis to determine the optimal cut-off value of fibrinogen, ferritin, D-dimer levels, and lymphocyte counts to predict large differences between SpO2 and SaO2(>4%) the best combination of sensitivity and specificity. Bland Altman method was performed to display bias (systematic error – mean difference between SpO2 and SaO2), precision (random error - standard deviation of mean difference) were calculated. Limits of agreement were defined at mean difference ±2SD.
The statistical significance level was expressed as p<0.05 for all tests.