Spss statistical package for windows v 26

The collected data was inserted into a spreadsheet and analyzed statistically using the IBM SPSS statistical package for Windows v.26 (Armonk, New York, USA). Nominal variables were expressed as frequency (percentage) and continuous variables as mean ± standard error of the mean (SEM). The normality of the data was tested using the Kolmogorov-Smirnov test. The statistical significance between the study groups was determined by using the Chi-square test for categorical variables and the Student’s t-test for continuous variables. A statistically significant result was considered if P ≤ 0.05. Logistic regression analysis was employed to assess the correlation between the measured biomarkers and the prognostic indices.

Data was analyzed using IBM SPSS statistical package for Windows v.26 (Armonk, NY, USA). A p ≤ 0.05 was considered statistically significant. Normally distributed continuous variables were represented as mean ± standard deviation of the mean, while non-normally distributed data was presented as median (interquartile range [IQR]). Categorical data was presented as frequency (percentage). Normality was tested using the Shapiro–Wilk test. Chi-square test, Fisher’s exact test or likelihood ratio were used to compare categorical variables accordingly. Non-parametric variables among the study groups were compared using Mann–Whitney U-test. On the contrary, independent t-test was used for parametric ones. Association of CBC variables and the ANC difference (compared to admission and sequential difference) is determined using univariate binary logistic analysis. Consequently, any significant variable was used in the multivariable regression model. Day 6 was eliminated from the univariate and multivariable analysis due to the low number of patient records that could be retrieved.