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Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting.Methods: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis.Results: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%.Conclusions: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.Trial Registration: PROSPERO ID: CRD42015029349 . [ABSTRACT FROM AUTHOR]

Background: Pre-eclampsia, a condition with raised blood pressure and proteinuria is associated with an increased risk of maternal and offspring mortality and morbidity. Early identification of mothers at risk is needed to target management., Methods/design: We aim to systematically review the existing literature to identify prediction models for pre-eclampsia. We have established the International Prediction of Pregnancy Complication Network (IPPIC), made up of 72 researchers from 21 countries who have carried out relevant primary studies or have access to existing registry databases, and collectively possess data from more than two million patients. We will use the individual participant data (IPD) from these studies to externally validate these existing prediction models and summarise model performance across studies using random-effects meta-analysis for any, late (after 34 weeks) and early (before 34 weeks) onset pre-eclampsia. If none of the models perform well, we will recalibrate (update), or develop and validate new prediction models using the IPD. We will assess the differential accuracy of the models in various settings and subgroups according to the risk status. We will also validate or develop prediction models based on clinical characteristics only; clinical and biochemical markers; clinical and ultrasound parameters; and clinical, biochemical and ultrasound tests., Discussion: Numerous systematic reviews with aggregate data meta-analysis have evaluated various risk factors separately or in combination for predicting pre-eclampsia, but these are affected by many limitations. Our large-scale collaborative IPD approach encourages consensus towards well developed, and validated prognostic models, rather than a number of competing non-validated ones. The large sample size from our IPD will also allow development and validation of multivariable prediction model for the relatively rare outcome of early onset pre-eclampsia., Trial Registration: The project was registered on Prospero on the 27 November 2015 with ID: CRD42015029349., Competing Interests: Not applicableNot applicableKM is Editor-in-Chief and KS is an associate editor for BMC Diagnostic and Prognostic Research. The other authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background: Early-onset pre-eclampsia with raised blood pressure and protein in the urine before 34 weeks' gestation is one of the leading causes of maternal deaths in the UK. The benefits to the child from prolonging the pregnancy need to be balanced against the risk of maternal deterioration. Accurate prediction models of risks are needed to plan management., Methods: We aim to undertake a multicentre prospective cohort study (Prediction of Risks in Early onset Pre-eclampsia (PREP)) to develop clinical prediction models in women with early-onset pre-eclampsia, for risk of adverse maternal outcomes by 48 h and by discharge. We will externally validate the models in two independent cohorts with 634 and 216 women. In the secondary analyses, we will assess risk of adverse fetal and neonatal outcomes at birth and by discharge., Discussion: The PREP study will quantify the risk of maternal complications at various time points and provide individualised estimates of overall risk in women with early-onset pre-eclampsia to plan the management., Trial Registration: ISRCTN registry, ISRCTN40384046., Competing Interests: The authors declare that they have no competing interests.

Background: The negative feedback system is an important physiological regulatory mechanism controlling angiogenesis. Soluble vascular endothelial growth factor (VEGF) receptor-1 (sFlt-1), acts as a potent endogenous soluble inhibitor of VEGF- and placenta growth factor (PlGF)-mediated biological function and can also form dominant-negative complexes with competent full-length VEGF receptors., Methods and Results: Systemic overexpression of VEGF-A in mice resulted in significantly elevated circulating sFlt-1. In addition, stimulation of human umbilical vein endothelial cells (HUVEC) with VEGF-A, induced a five-fold increase in sFlt-1 mRNA, a time-dependent significant increase in the release of sFlt-1 into the culture medium and activation of the flt-1 gene promoter. This response was dependent on VEGF receptor-2 (VEGFR-2) and phosphoinositide-3'-kinase signalling. siRNA-mediated knockdown of sFlt-1 in HUVEC stimulated the activation of endothelial nitric oxide synthase, increased basal and VEGF-induced cell migration and enhanced endothelial tube formation on growth factor reduced Matrigel. In contrast, adenoviral overexpression of sFlt-1 suppressed phosphorylation of VEGFR-2 at tyrosine 951 and ERK-1/-2 MAPK and reduced HUVEC proliferation. Preeclampsia is associated with elevated placental and systemic sFlt-1. Phosphorylation of VEGFR-2 tyrosine 951 was greatly reduced in placenta from preeclamptic patients compared to gestationally-matched normal placenta., Conclusion: These results show that endothelial sFlt-1 expression is regulated by VEGF and acts as an autocrine regulator of endothelial cell function.