Results are presented as mean and standard deviation or median, according to normality characteristics for each variable. Student's t-test was used to compare parametric variables between two groups and the Mann-Whitney test was used for non-parametric variables. Categorical variables were expressed as proportions and compared with the chi-square or Fisher's exact test. Variables with significant univariate associations were considered as candidates for multivariable analysis. Longitudinal multivariable logistic regression was performed using backward variable selection, with the exit criteria set at p < 0.25. Variables not selected by the automated procedure were added back into the models individually to evaluate residual confounding and covariance, and we tested for colinearity among all variables using univariate analysis to identify possible associated confounding variables. Subsequently, through the construction of a logistic regression model, multivariate analysis was performed with odds ratio (OR) calculations, including all independent variables that showed association with the mortality, with p ≤ 0.20.
All statistical analyses were performed on an intention-to-treat basis using SPSS 17.0 for Windows statistical software (SPSS, Chicago, IL, USA), with a two-sided p < 0.05 considered to be statistically significant.
All statistical analyses were performed on an intention-to-treat basis using SPSS 17.0 for Windows statistical software (SPSS, Chicago, IL, USA), with a two-sided p < 0.05 considered to be statistically significant.