General practitioners were informed about the participation of their patient in the study with a standard letter approved by ethics committee (See Appendix 7). If any abnormal blood results were identified then the GP was informed of the results with a copy of the GP letter and copy of the results.
75 2.8 Statistical analyses
Statistical analyses were performed using the Statistical Package for Social Sciences (SPSS Version 18.0.0, 2009, SPSS Inc., Chicago, USA) and Medcalc software for windows (Medcalc Belgium, Version 11.6). The data on recruitment, conception, protocol adherence and outcome are presented in numbers and percentages. The data was assessed for normality of distribution by using frequency histograms and Kolmogorov-Smirnov test. Data are expressed as means ± standard deviation (SD) if normally distributed, and median ± inter-quartile range (IQR) or range if appropriate.
One-way analysis of variance (ANOVA) test was used to determine statistically significant difference between the means of three or more independent groups. This test was used for normally distributed continuous variables with equality of variances between the independent groups. When the overall difference between all the means was significant a post-hoc Bonferroni test was used to identify which two means were significantly different.
Kruskal-Wallis one-way analysis of variance was performed to determine statistically significant difference between non-normally distributed means of three or more independent groups. This test was also used when the groups were of unequal size.
Univariate general linear model (GLM) was performed to determine statistically significant difference between the means of two independent groups when the continuous dependent variable was affected by two or more factors. The mean AIx and mean aPWV between the groups were compared accounting for the fixed factors and covariates affecting them.
A repeated measures analysis of variance was performed to determine statistically significant difference between three or more observations of a dependent continuous variable in the same participant over time. When the overall difference between all the means was significant a post-hoc Bonferroni test was used to identify which two means were significantly different.
Paired or independent sample t-test was performed to determine statistical significance between the means of two observations if the data was normally
76 distributed and Mann-Whitney U test was performed if the data was not normally distributed or if the sample size was small.
The association between normally distributed variables was determined by Pearson’s correlation coefficient (r) and that between non-normally distributed or small sample size was determined by Spearman’s correlation coefficient rho (). The value of the correlation coefficient varies between -1 to +1, with 1 being the strongest and 0 being no correlation between the variables. The sign
“-“ is suggestive of inverse and “+” is suggestive of positive correlation.
For all statistical tests P < 0.05 was determined as statistically significant. All P values were two-tailed.
Z-Score is the probability of a score of data occurring within the normal distribution and was determined using the observed and expected values from the means and SD of the population as described for the CRL measurement at 10-14 weeks and birthweight.
Bland Altman Plot232 is a statistical method of plotting and comparing measurements obtained by two different techniques. The scatter plot represents the correlation between the two different methods. The mean of the two measurements is plotted on the X-axis and the difference between the measurements is plotted on the Y-axis. The limits of agreement are the 95%
confidence interval of the difference between the two methods (average difference ± 1.96 SD of the difference). A narrower the 95% confidence interval implies better agreement between the two techniques.
2.8.1 Cardiovascular data
Pre-pregnancy comparisons: The pre-pregnancy measurements of MAP, central SBP, HR, CO and PVR in the four groups were compared using one-way analysis of variance (ANOVA) test and a post-hoc Bonferroni test was used to determine statistically significant differences between the groups. The AIx and carotid-femoral (aortic) aPWV between the groups were compared using ANOVA – generalized linear model univariate analysis using age, height and HR as covariates for AIx and MAP as a covariate for aPWV. The pre-pregnancy measurements of cardiovascular tests were also compared between the two groups: those with normal pregnancy outcome and those
77 with PE and/or IUGR in their current pregnancy using independent student t- test or Mann-Whitney U-test.
Longitudinal analysis: For the purpose of longitudinal analysis nulliparous and multiparous women with normal current pregnancies were considered as normal control group and the longitudinal changes were analyzed in this group separately as opposed to the other two groups- normal pregnancies in women with previous history of RM and normal pregnancies in women with previous IUGR/PE.
Longitudinal data on cardiovascular assessment, blood biochemistry and platelet aggregation data were analyzed using repeated measures ANOVA with the post-hoc Bonferroni test for pair wise comparisons. For the cases where the cardiovascular data was missing the data was imputed by calculating the estimated value using the mean difference of changes at that time in the pregnancy.233
2.8.2 Ovulation and implantation timing data
The ovulation, implantation timing and O-I interval in the ongoing pregnancies at 10 weeks, pregnancies that miscarried at less than 6 weeks and pregnancies that miscarried after 6 weeks were compared using Kruskal-Wallis Test.
Bland-Altman plots were constructed to compare the GA predicted by CRL (GACRL), GA estimated by adjusting for ovulation timing (GAOV) and GA estimated by adjusting for implantation timing (GAIMP).232 The 95% confidence intervals (CI) were calculated of the differences between the gestational age estimated by single CRL measurement and that estimated based on adjustments for ovulation timing and implantation timing.
2.8.3 Ultrasound scan and birth weight data
Growth rate (mm/day) was calculated by dividing change in CRL by change in GA in the first trimester, changes in AC by change in GA in second and third trimesters respectively. The first trimester CRL growth rate was calculated as an average of the growth rate between scan 1 and 2 and the growth rate from scan 2 to 3. The second and third trimester growth rate was calculated as an
78 average of growth rate of AC between scan 1 and 2 in the second trimester and scan 2 to scan 3 in the third trimester.
CRL z-score at the 10-14 week scan was calculated as (measured CRL – expected CRL)/standard deviation, with respect to the Robinson & Fleming curve.80 The birthweight z-score was calculated from the unstandardized residuals derived from the correlation between observed birthweight and observed GA at delivery. The relationship between implantation day, O-I interval, first-trimester growth, birthweight z-score, length of gestation and cardiovascular changes was investigated using spearman’s correlation coefficient for non-normally distributed data and Pearson’s correlation coefficient for normally distributed data.
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