Spss version v23

Categorical data are presented as numerical values and percentages. Normally distributed data were analyzed by with a two-tailed unpaired Student t-test. The significance threshold was set at P < 0.05. A receiver operating characteristic (ROC) curve was plotted to build a prediction model assessing the prognostic value of demographics and laboratory findings in predicting 30-day mortality in hospitalized patients with COVID-19. All data analysis was performed in SPSS version v23 (IBM Corp, Armonk, NY, USA). The main biological markers (CRP, fibrinogen, D-dimer, FVIII, vWFAg, and FVIII/vWFAg) were compared according to patient admission type and oxygen requirements at admission through analysis of the means. We assessed the association of biological markers measured at ICU admission with an increase in oxygen requirement, thrombotic events, and all-cause mortality within 30 days after admission.

After performing quality checks on the dataset, descriptive statistics were calculated. Measures of central tendency and dispersion for continuous variables are provided as the mean and standard deviation for normally distributed data, while median with range for data that did not conform to normality assumptions and categorical variables as number and percentage. To test the significance of association between categorical variables, we used the Chi-square test or Fisher’s exact. Independent-samples Mann–Whitney U-Test and Kruskal–Wallis Test were used to test the significance of the difference between two and more than two categories of continuous variables. Correlation between various continuous variables was calculated using Spearman correlation coefficient. A p-value of less than 0.05 was considered to be statistically significant. All data analysis was performed using SPSS version v23 (IBM Corp, Armonk NY, USA).