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.