Title: Individualised risk prediction of fetal infection and development in pregnancies that deliver preterm using advanced MRI techniques and machine learning
Funder: Department of Health and Social Care

Abstract: Research Question Can fetal MRI be utilised to establish an individualised, aetiology-driven prediction of infection and adverse neonatal outcomes associated with spontaneous preterm birth (sPTB)Background Very preterm birth ( Aims and Objectives In a cohort of pregnancies that subsequently deliver very preterm to utilise MRI to: assess placental, fetal thymus, lung and regional brain macro and microstructure and perfusion, and relationships with proven infection. utilise machine learning to develop predictive tools, identifying: fetal infection. fetuses at highest risk of adverse outcome. Methods 100 women with uncomplicated pregnancies will be recruited and 75 at high risk of sPTB (16-32 weeks gestation) including those with: risk factors for sPTB (previous premature delivery or cervical surgery) and a high risk of delivery, determined by clinical predictive algorithms (https://quipp.org) or preterm premature rupture of membranes or exposed membranes through premature cervical shortening. Fetal MRI will be undertaken including T2, T2* (reflecting perfusion) and diffusion (reflecting microstructure) imaging of the fetal brain, thorax and placenta. Post-processing pipelines will generate thymus, placenta, lung and regional brain volumes and apparent diffusion co-efficients, fractional anisotropy values and T2* maps of the same regions. Imaging will be repeated every two weeks (maximum three) in the high-risk cohort if undelivered. Postnatal confirmation of infection will be ascertained from placental histology. Clinical outcome parameters including mortality, abnormalities on brain imaging, bronchopulmonary dysplasia or retinopathy of prematurity will be recorded to obtain a postnatal composite of adverse outcome. Additional datasets will be obtained from pregnancies imaged in other research studies (>650 datasets). Results of antenatal imaging findings and postnatal outcome measures will be used to generate predictive models for adverse outcomes. Models will be tested with datasets from collaborators (with >10% of the numbers for model development) prior to larger multicentre trials. Timelines for delivery Month 1-36 patient recruitment, MRI phenotyping of preterm and control pregnancies. 36-48 data analysis, predictive-model development, model testing, dissemination of findings, planning of RCTs. Impact and Dissemination Outputs will be disseminated through a study website, peer-reviewed journals, international conferences and through social media. The preterm birth PPI group and national pregnancy charities will also disseminate findings. Development of an effective prediction imaging tool for adverse fetal outcomes following sPTB would be followed by RCTs to evaluate if outcomes of pregnancies identified 'at risk' are improved by management strategies such as timing of delivery, or antenatal therapies.

Lay abstract: One in every two hundred babies has a very premature birth (PTB) (less than 32 weeks of pregnancy). This can result in severe life-long complications including cerebral palsy, learning and behavioural difficulties and breathing problems. Besides immeasurable human costs for the baby and family this also has significant cost implications for the NHS. Infection may cause both the early delivery and associated abnormal brain and lung development. Where obvious signs of infection are not evident in the mother, subtle, undetected infection may be present in the unborn baby. Currently there is no clinical test to detect infection of the baby inside the womb. Fetal Magnetic Resonance Imaging (MRI), already used in clinical practice, can produce clear images of the baby's brain and lungs. It can also assess blood flow and tissue structure in detail, as well as organ size. My initial data suggests that areas of the brain and lungs are smaller in babies that subsequently deliver very preterm, indicating factors responsible for preterm birth may already be affecting how the baby develops in the womb. MRI can also give information regarding infection in the fetus by measuring the thymus, an organ in the neck vital to the immune system, and by scanning the placenta. I have exciting new data that suggests that measurements in these organs could identify infection.Women who have previously undergone preterm birth have helped to plan this study. Aims I want to find out if I can predict which babies have an infection in the womb and which are likely to develop complications after preterm birth. Using MRI, I will look for differences in placenta, thymus, lung and brain development in fetuses that deliver preterm and those delivering at term. I will see if these scans can accurately identify babies that have an infection and those who do poorly after birth. Study Design 75 pregnant women at high risk of PTB (between 16-32 weeks of pregnancy) and 100 women with uncomplicated pregnancies will be invited to participate. Women will be identified as high-risk if: they have no symptoms but are attending the Prematurity Clinic at St Thomas's Hospital London with risk factors for PTB (previous premature delivery or surgery to the neck of the womb (cervix)), and are likely to deliver early; we can pick this up by seeing if the cervix has already shortened, and by doing a vaginal swab test or they have lost the fluid (waters) around the baby or the cervix has opened before 24 weeks. (normally this occurs after 37 weeks). These women will have an MRI scan of the fetal lung, brain, thymus and placenta. Imaging will be repeated, in the high-risk group, every two weeks if undelivered, (maximum of three scans). After birth I will see if infection is present by analysing the placenta under a microscope. I will collect information about complications in the babies from medical notes. All of this information, as well as that from scans in healthy pregnant women involved in other research studies, will be combined in a new test, using 'machine learning', involving computer analysis of the data to see if babies most likely to have problems after preterm birth can be identified. Results will be presented in scientific papers, conferences and via social media. If the test is proven to work, the next step will be to find out whether it can help determine the best time to deliver the baby; this may be sooner if fetal infection is found. The appropriate timing of existing treatments to prevent brain and lung injury would be facilitated in future clinical trials.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: REPRODUCTIVE HEALTH AND CHILDBIRTH