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).