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Prediction and prevention of preeclampsia and other adverse pregnancy outcomes

Aim To assess current methods of prediction of adverse pregnancy outcomes, develop a prediction model and assess diet and life style in preventing preeclampsia. Methods Meta-analyses performed to assess the role of abnormal 1st trimester biomarker levels in predicting PE and the predictive accuracy of 2nd trimester UAD indices for stillbirth. A prospective observational study was performed to assess the efficacy of maternal characteristics, biomarkers, arteriography and UADs for predicting adverse pregnancy outcomes. Previously published 1st trimester PE prediction models were validated using data collected from the observational study. A systematic review on the effect of diet and life style based metabolic risk modifying interventions on PE was performed. Results The review of biomarkers found that abnormal levels were particularIy associated with early onset PE. The stillbirth review demonstrated a three-four fold increased risk of still birth with abnormal UAD. 1045 women were included for analysis in the prospective observational study. Our models' detection rate (false positive rate of 15%) was 72% for PE; 48% PIH; 30 % SGA < 10th centile; 57% SGA < 5th centile and 67% stillbirth. In the validation study the observed discrimination ability in the derivation studies ranged from 0.70 to 0.954. When validated against the study cohort, the AUC varied importantly, ranging from 0.504 to 0.833. Dietary interventions were shown to reduce the risk of PE by 33%, with no reduction in risk with mixed interventions or fatty acid supplementation. Conclusion The high heterogeneity of studies in the systematic reviews makes it difficult to draw firm conclusions regarding the use of biomarkers or UADs in screening for pregnancy complications. Our prospective study showed a role for haemodynamics as part of routine 1st trimester screening for assessing the risk of hypertensive disease in pregnancy.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:766103
Date January 2018
CreatorsAllen, Rebecca Emma
PublisherQueen Mary, University of London
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://qmro.qmul.ac.uk/xmlui/handle/123456789/33944

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