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.