Utero-placental Vascularisation in Normal and Preeclamptic and Intra-uterine Growth Restriction Pregnancies

Overview

Introduction Preeclampsia (PE) and intra-uterine growth restriction (IUGR) are two major pregnancy complications related to chronic utero-placental hypoperfusion. Three-dimensional power Doppler (3DPD) angiography has been used for the evaluation of utero-placental vascularisation and three vascular indices have been calculated: the vascularisation index (VI), flow index (FI) and vascularisation-FI (VFI). However, several technical endpoints hinder the clinical use of 3DPD as physical characteristics and machine settings may affect 3DPD indices, and so its clinical significance is not yet clear. Objectives The primary objective is to better understand the clinical significance of 3DPD indices by evaluating the relationship between these indices and placental morphometry. Secondary objectives are (i) to determine the impact of machine settings and physical characteristics on 3DPD indices, and (ii) to evaluate physio-pathological placental vascularisation patterns. Methods and analysis This is a prospective controlled study. We expect to include 112 women: 84 with normal pregnancies and 28 with PE and/or IUGR (based on our former cohort study on 3DPD indices for PE and/or IUGR prediction (unpublished data)). Within 72 h before planned or semi-urgent caesarean section, utero-placental 3DPD images with five different machine settings will be acquired. Placentas will be collected and examined after surgery and stereological indices (volume density, surface density, length density) calculated. The 3DPD indices (VI, FI and VFI) of the placenta and adjacent myometrium will be calculated. Correlation between Doppler and morphological indices will be evaluated by Pearson or Spearman tests. Agreement between 3DPD indices and morphological indices will be assessed by Bland and Altman plots. The impact of Doppler settings and maternal characteristics on 3DPD indices will be evaluated with a multivariate linear regression model.