Spss v26 statistical software package

Data are presented as mean ± standard deviation (numerical variables with Gaussian distribution), median and interquartile range (numerical variables with non-Gaussian distributions), respectively, percentage from the sub-group total and number of individuals. Continuous variables distribution was tested for normality using Shapiro–Wilk test and for equality of variances using Levene’s test. Adjusted risk estimates for all-cause mortality were calculated using univariable and multivariable Cox regression. In this study, a p value of 0.05 was considered the threshold for statistical significance. Data were analyzed using SPSS v26 statistical software package (SPSS Inc, Chicago, IL, USA).

Variables were analysed with the Chi-square test. All continuous variables were tested for normality using Shapiro–Wilk test. Data is presented as average ± standard deviation (SD), median and percentage for normally distributed continuous variables. For non-normally distributed continuous variables, the median and interquartile ranges (IQR) were reported, and groups were compared using the Wilcoxon Signed Ranks test. A log-rank test was conducted to determine the differences in the survival distribution in the three groups. Pairwise comparisons were conducted to determine which group had different survival distribution. The mortality risk was analysed using multivariate Cox proportional hazards models. In order to assess the independent factors that predict the risk of death in our cohort we employed a backward multivariate logistic regression model. An Akaike information criteria (AIC) was used in order to determine the best model. Odds ratio and 95% confidence interval (CI) were calculated. A linear ANOVA model was performed in order to determine the impact on hospitalizations. In this study, a p-value of 0.05 was considered the threshold for statistical significance. Data was analysed using SPSS v26 statistical software package.