 Hi everyone, my name is Rebecca Harris and I'm a PhD candidate at the University of Wollongong in Australia. Today I'm going to be talking about a multilevel multivariate meta-analysis that we conducted with the metaphor package in R to evaluate the impact of Brieklampsia on offspring blood pressure. In studies pertaining to cardiovascular health, systolic and diastolic blood pressure are key outcomes of interest and are both usually reported in primary studies such as randomized trials and cohort studies. And these outcomes are characterized as dependent. What we mean when we talk about dependence is that the outcomes are correlated, in this case highly correlated, and this means that knowing information about one outcome reveals some information about the other. When conducting a naive, pair-wise meta-analysis, dependent outcomes cannot be pulled together because doing so would violate the assumption that effect sizes are independent and from a different sample, and this is because the model doesn't distinguish between different data structures. So the actual structure of the data is as shown here, where systolic and diastolic blood pressure are measured in the same participants. But in a naive, pair-wise meta-analysis, the statistical model assumes that the effect sizes are from a different unrelated sample. And this is true even if the outcomes are different. And this is problematic mainly due to the artificial increase in the sample size, and this leads to confidence intervals that are much too narrow. An appropriate approach you can buy in multiple dependent outcomes in a meta-analysis model is through multilevel modeling, which accounts for the dependence between the outcomes by specifying in the statistical model how each effect size is nested in the included study. So as shown in the illustration here, if a study reports both systolic and diastolic blood pressure, we can add another level of variance at level two. So this is basically an extension of a random effect two-level model with an additional tau squared variance component for the multiple outcomes. So now we have three levels of variance. The correlation coefficient between systolic and diastolic blood pressure is not required, and this is because this is advantageous because correlations are often not reported in the original studies. We used a case study of preclinemia and offspring blood pressure to illustrate an example of systolic and diastolic blood pressure in a multilevel meta-analysis. So a bit of a background about preclinemia is a health condition during pregnancy characterized by new onset hypertension, so hypertension that occurs after 20 weeks of gestation, and occurs alongside maternal organ dysfunction or fetal growth restriction. Although the cause is not well understood, observational cohort studies have provided some evidence that preclinemia is associated with an increase in blood pressure through childhood and adolescence. There have been some previous meta-analyses on this topic, which used standard pair-wise univariate meta-analysis, and this means that they dealt with the dependence between systolic and diastolic blood pressure by conducting separate analyses for each outcome. Another important component of the structure of the data in this particular case study is the longitude and nature of the effect measures. But because participants in the studies were followed for a long period of time, there were often multiple measures of both systolic and diastolic blood pressure in each sample. Most of the previous meta-analyses selected one time point to extract data from, but there was one previous meta-analysis which included multiple follow-ups that they were independent and therefore violating the independence assumption. Longitude and nature data can be included in a multilevel meta-analysis by just considering that the different time points as different outcomes also nested within each sample. So this brings us to our current project, and our aim was to conduct a systematic review and meta-analysis to compare blood pressure of offspring board to pre-climatic and normative pregnancies. We registered our protocol in Prosperum, and to search for articles we searched the PubMed, Sinal, and M-based databases from their inception to January 31st, 2022. And we searched the citations of included cohort studies and previous reviews, as well as conducted forward citation searching using Google Scholar. For our selection criteria, participants could be any age including from infancy to older age, and they were included if they reported systolic or diastolic blood pressure on a continuous scale, and this could be millimeters of mercury, but could also be percentile scores or standard deviation scores. We conducted tidal and abstract screening using abstractor, and this was done by two review authors in duplicate independently. And to assess the within study risk bias, we used the Robin's e-tool for observational studies, and this was again conducted by two review authors in duplicate. When looking at observational evidence, it's very important to take into account that there could be confounders which affect the relationship between the exposure and the outcome. So this is the graph showing the confounders and mediators of interest. The main confounders of interest were maternal smoking and drinking during pregnancy, education or socioeconomic status, maternal parity, age, ethnicity, or pre-pregnancy BMI. We also made the important distinction between confounders and mediators, because mediators lie on the causal pathway, so they could, because they occur after development of PE, it's not appropriate to adjust for these. So therefore, we only pulled results from studies which had adjusted for all relevant confounding factors and did not adjust for any mediators. For our analysis, we chose to pull Hedges G standardiseming differences to enable us to pull effect sizes which were expressed as percentile scores and standard deviation scores with those from other cohorts, which were expressed in millimeters of macro. And because effect sizes were adjusted for confounders using multiple regression models, we also included a correction factor when calculating these to account for the fact that the variability around the mean difference decreases as the number of variables in the regression model increases. So this is the code we used to conduct the multi-level meta-analysis. And also on the right, you can see the data set and how it was set up in long format. So we first created dummy variables for systolic and diastolic blood pressure to be used as moderators in the model. And this enabled us to get a pulled effect for both outcomes separately. And then to run the meta-analysis, we used the rma.mv command available in the metaphor package. And we specified the effect size and variance shown here. And we used the mod's argument for the regression to obtain different effects for systolic and diastolic blood pressure. And importantly, we used the random argument. And this was to specify how each effect size was nested inside each cohort. And we used restricted maximum likelihood estimator for the level 2 and level 3 variability. So these are the results from our study selection. There were 2,423 unique reports identified from database searching and 12 unique reports from citation searching. And after full text screening, there were 55 reports of 42 cohorts. However, like I mentioned before, because we only pulled data from cohorts which were adjusted for confounders, there were only seven cohorts included in data analysis. Here are the results from our multi-level meta-analysis as shown in the forest plot. So here we have the systolic blood pressure and the standardised mean difference and the diastolic blood pressures standardised mean difference pulled. So there were only small increases in both systolic and diastolic blood pressure. And when we re-expressed the standardised mean differences into millimeters of mercury, we had a 1.69 millimeter of mercury difference in systolic blood pressure and a very small 0.65 millimeters for diastolic blood pressure. So here are the results from our risk of bias assessment. Overall, there was a low risk of bias in most domains, but there were some concerns due to missing data, mostly due to the long follow-up of the cohorts. And this led to all studies being rated as some concerns overall. The main interpretation of our findings is that offspring born to pre-eclectic pregnancies do have higher systolic and diastolic blood pressure when compared to norm-attensive pregnancies. But this difference was much smaller than previously reported due to the fact that we correctly specified how dependent outcomes were nested in the cohorts. And it's not clear if these small differences in blood pressure are significant enough to infer a clinically meaningful difference in adverse cardiovascular outcomes in later lives, such as heart attack and stroke. Here are our references, and thank you so much for watching.