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.