Prediction and prevention of fetal growth restriction and compromise of fetal wellbeing

Fetal growth restriction (small for gestational age babies) or FGR is a major cause of death and disability for newborn babies. At present its detection relies on basic ultrasound in the antenatal period which has variable accuracy. There are many studies looking at other tests (e.g. blood tests, advanced ultrasound tests, examination of the mother‘s abdomen) and how accurate these are at predicting or diagnosing whether a baby will be born small and/ or suffer problems in the newborn period. All the studies on this topic will be collected and assessed to see how accurately tests performed and the results combined to show an overall estimate of the accuracy for predicting and diagnosing FGR. Information on effectiveness of different treatments to prevent and treat FGR will also be collected and summarised. The above information will be used to develop analytical models where decisions are based on trade-off between expected benefits and harms. This should lead to production of informed and integrated clinical care pathways so that pregnant women and their unborn babies can be tested and treated in the best way possible while minimising associated complications.

Background: Restriction of fetal growth and compromise of its wellbeing remain significant causes of perinatal death and childhood disability. At present, there is a lack of scientific consensus about the best strategies for predicting these conditions before birth. Therefore, there is uncertainty about the best management of pregnant women who might have a growth-restricted baby. This is likely to be due to a dearth of clear collated information from individual research studies drawn from different sources on this subject.
Scientific Objectives: The proposed project will systematically review all the published literature in this field to arrive at the most accurate tests for predicting fetal growth restriction and compromise of its wellbeing and integrate diagnostic and therapeutic information using decision-analytic modelling.
Methodology: The guidance on systematic reviews published by the Centre for Reviews and Dissemination, the University of York and The Cochrane Collaboration will be followed. There will be reviews in three areas, prediction, diagnosis and treatment. The following recommended steps will be used: framing the questions for reviews; identifying relevant literature; assessing the quality of the literature; summarising the evidence; interpreting the evidence by decision-analytical modelling. The diagnostic data will be analysed by pooling sensitivity (true positive rate) and specificity (true negative rate), taking account of their correlated nature and expected heterogeneity using bivariate models. The therapeutic data will be evaluated using relative risks (RR), their 95% CIs, and Forest plots using Review Manager Software. When feasible meta-analyses will be undertaken. Statistical tests will be used to check for heterogeneity, random or fixed effects model will be used appropriately. Funnel plots will examine for publication bias. Results will be re-expressed as likelihood ratios and numbers needed to treat where possible. The diagnostic and therapeutic information generated from systematic reviews will be integrated using decision-analytic modelling to develop an evidence based approach to the detection and treatment of fetal growth restriction.
Scientific and medical opportunities of study: By critically evaluating the published research, this project will also provide a framework for setting up robust studies in this field of perinatal care in the future. There will be close collaboration with the Health Economics Facility (Professor S Bryan), Birmingham Clinical Trials Unit (Professor R Gray) and the Public Health and Epidemiology Department (Professor J Deeks and Dr C Meads). These units will provide direct access to expertise, supervision and guidance in data analysis, economic evaluation and the methodological components in the comprehensive training program.