Improving the prediction and prevention of adverse pregnancy outcomes Evidence from systematic reviews and primary studies

Most women go through pregnancy without any complication, however in some cases the mother or unborn baby may be at increased risk of adverse outcome. Antenatal care is aimed at identifying these women with an increased risk of adverse pregnancy outcome. If clinicians can accurately categorise women into high or low-risk groups, women at high-risk can receive closer monitoring or treatment to reduce complications, while those at low-risk can have only routine care, thereby avoiding unnecessary treatment. With access to individualised risk estimates, clinicians will be able to provide targeted personalised care.
Risk factors for adverse pregnancy outcomes are usually combined in mathematical prediction models to assist clinicians in making individualised risk predictions, and although clinicians intuition has a place in clinical prediction, statistical models are more accurate in predicting outcomes than what can be achieved without these tools.
This thesis explored risk prediction in three groups of women: those who enter pregnancy apparently healthy; those diagnosed with a high-risk condition for the first time in pregnancy i.e., pre-eclampsia and those who enter pregnancy with a pre-existing medical condition i.e., epilepsy.
The main objective of the research described in this thesis is to provide tools that can improve pregnancy outcomes, by developing and validating prediction models with clinically relevant predictors, in addition to evaluating the magnitude of risk of complications for high-risk conditions in mother (epilepsy) and offspring (prematurity), and evaluating a novel health technology in preventing preterm birth.