2.5 Statistical methods
For the purposes of this study, patients were divided into tertiles according to their PDI and the data was analyzed accordingly. Continuous variables were given as mean ± SD or median and IQR depending on the pattern of distribution. Categorical variables were presented as number and percentages. Patterns of distribution and equality of variances for continuous variables were tested using Shapiro-Wilk and Levene’s tests, respectively. For continuous variables, either one-way ANOVA with post-hoc Tukey test or Kruskal-Wallis test were used as appropriate to determine significant differences between groups. For variables with a skewed distribution, Mann-Whitney U test was used to find the exact difference between groups if Kruskal-Wallis test suggested a significant difference. Categorical variables were analyzed using χ2 test and standardized residuals were calculated to determine deviations from expected values. Correlations between echocardiographic and catheterization variables with PDI were analyzed using Spearman’s Rho. Kaplan-Meier curves were drawn to analyzei) survival to postoperative day 15, ii) survival free of RVF at postoperative day 15, iii) total long-term survival for PDI tertiles. Log-rank test was used to determine significant differences between PDI tertiles in terms of survival. Cox proportional hazards models were built to analyze the association of demographic, clinical, laboratory and echocardiographic with short-term survival and RVF-free survival. A univariate analysis was initially done to select parameters that were associated with events, and parameters with a p value less than <0.1 were included in the final multivariate Cox regression analysis. These models did not include echocardiographic systolic pulmonary artery pressure and right ventricular end-diastolic minor diameters due to collinearity of these parameters with PDI.
All statistical analyses were performed using SPSS 17.0 for Windows (IBM Inc, USA). For all comparisons, a p value below 0.05 was accepted as statistically significant.