Statistical Analysis
Analyses were performed using Statistical Package for Social Sciences
software, version 20.0 (SPSS; IBM, Armonk, New York, USA). Baseline
characteristics and echocardiographic parameters were compared among the
patients by parity number and categorized; accordingly, nulliparous, 1
to 4 and parity>4. Kolmogorov-Smirnov test was used for
testing of normality. Continuous variables were expressed as mean± SD
and compared using one-way analysis of variance. Tukey Post-hoc test was
performed to reveal the statistical difference between the groups.
Continuous variables with skewed distributions compared using the
Kruskal-Wallis test and Bonferroni-corrected Mann-Whitney U test was
performed to reveal the statistical difference between the groups.
Categorical variables were expressed as number and percentages and
Pearson’s chi-square or Fisher’s exact tests were used to evaluate the
differences. Hierarchical logistic regression analysis was used for
multivariable analysis to evaluate the univariable and multivariable
confounders for RV dilation. The odds ratio (OR) indicates the relative
risk of RV dilation. Multivariate analysis by stepwise logistic
regression models (backward elimination) tested variables that were
significant at p<0.1 in the univariate analysis. A forward
hierarchical logistic regression model was used for multivariable
analysis to assess the independent relationship between each parity
category and RV dilation and hypertrophy. Two models were generated to
obtain the impact of potential confounders on the association between
parity category and RV dilation and hypertrophy. These 2 models include:
(1) unadjusted; (2) adjusted for age, body mass index, body surface area
and smoking. The odds ratio (OR) indicates the relative risk of RV
dilation and RV hypertrophy of parity category compared with
nulliparity. Intra-observer and interobserver variability were assessed
on separate occasions, using new arbitrary images for RV basal dimension
and RV thickness blinded to the previous results and shown in Table 4.
Fifty subjects were randomly selected from each group for the analyses.
For the interobserver variability assessment, the first observer
performed the analyses. Second observer repeated the analyses within 24
hours. For assessment of the intra-observer variability the analyses
were repeated twice by the first observer within 1 week. Results were
analyzed using coefficient of variation where differences between
measurements were expressed as the ratio of the standard deviation to
the means and multiplied by 100. Statistical significance was defined as
a p value < 0.05.