Statistical analysis
Variables were checked for the normality of distribution using Kolmogorov-Smirnov test. Continuous data were presented as mean ± standard deviation if normally distributed, while skewed data were presented as median and corresponding interquartile range. In order to compare variables between different NYHA classes, we used one-way ANOVA or Kruskal-Wallis test, as dictated by distribution, with a Bonferroni post-hoc correction. We used Pearson’s correlation coefficient to assess correlations between continuous variables. Categorical data were expressed as numbers and percentages and they were compared using either\(\chi^{2}\ \)test or Fisher exact test, as appropriate.
Correlates of severe HF symptoms were assessed using binary logistic regression. Receiver operating characteristic (ROC) curves and the respective area under the curve (AUC) were used to assess the accuracy of each parameter to identify severe symptoms of HF. A cut-off value for each parameter was chosen based on the highest sum of sensitivity and specificity. Variables with statistical significance in univariable analysis were included in the multivariable model, which also included age – regardless of its significance in univariable analysis. Results were reported as odds ratios (OR) with 95% confidence intervals (CI). All statistical analysis was performed using SPSS version 20.0 statistical software package and P-values<0.05 were considered statistically significant.
Intra- and interobserver reproducibility of RVPAC was evaluated in 10 randomly selected patients, using intraclass coefficient (ICC) on a two-way mixed-effects model. We found a good intra- and interobserver reproducibility (ICC=0.90 [95% CI, 0.61–0.98] and ICC=0.84 [95% CI, 0.41–0.96], respectively).