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Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise BIRTH management: the MIBIRTH study

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AI plain-English summary

A third of UK births now involve emergency caesarean sections or assisted deliveries, often after prolonged labour and under anaesthetic outside normal hours. These complex births carry serious risks: babies may be admitted to neonatal units with infections or brain injuries, while mothers can suffer excessive bleeding, pelvic floor damage, and lasting physical or emotional trauma. The core problem is that before labour starts, doctors cannot reliably predict which pregnancies will run into trouble—whether from a poorly functioning placenta or a baby that does not fit through the birth canal. This study uses MRI scans after 36 weeks of pregnancy to measure the mother’s pelvis, the baby’s head and position, plus fetal energy reserves and placental function. By combining these images with routine clinical data, the team will build a personalised risk score for each woman. If successful, this tool would let clinicians and mothers make informed decisions before labour begins—potentially avoiding difficult deliveries and their lifelong consequences. The research does not aim to eliminate all interventions, but to reduce the number that happen as emergencies, shifting care from crisis management to planned, safer choices.

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In 2020 there were 464,437 U.K. births, of which one third were delivered by Emergency Caesarean Section ( EmCS) or assisted delivery (forceps or vacuum extraction). These interventions often follow a prolonged exhausting labour with a procedure performed under anaesthetic outside normal working hours. Such complex births are more common in older mothers, mothers who are obese and those with small or large babies. They are associated with an increase in baby's admission to the neonatal unit with infection and injuries to the baby's brain, such as hypoxic -ischaemic encephalopathy (HIE). Mothers may suffer excessive bleeding, pelvic floor injuries with subsequent urinary and/or faecal incontinence/leaking, painful sexual intercourse and post-traumatic stress disorder, with lifelong impacts to physical and emotional health. Approximate 17% of women have an elective section (ElCS), leaving only 50% of women in the U.K. having unassisted vaginal deliveries. The main reasons for EmCS or assisted delivery are concerns about baby's wellbeing in labour with compromise relating to a poorly functioning placenta and difficulties with the baby fitting through the birth canal (cephalopelvic disproportion, CPD). Before labour starts, it is currently difficult to accurately predict which babies will get into difficulty or to identify pregnancies at risk of CPD. Other factors such as the mother's age and weight are also important. Creating an individualised risk for EmCS or assisted delivery for a woman at the end of pregnancy would dramatically improve joint patient/clinician decision-making with the potential to avoid a difficult delivery, and improve maternal and neonatal outcome. We plan to use a technique called magnetic resonance imaging (MRI) in late pregnancy, after 36 weeks of gestation (GA), to identify mothers at increased risk of a difficult delivery. This imaging does not use radiation and is safe for both mother and baby. It is currently widely used in clinical practice to diagnose fetal abnormalities and well tolerated. We will scan 500 women who have a higher risk of an adverse delivery and combine measures of the maternal pelvis, fetal head and fetal position with assessment of fetal energy reserves and heart function, and placental function. We will approach mothers at around 20 weeks GA about the study. This will allow them time to read the patient information sheet, watch a video about antenatal MR scanning, and discuss the study with their partners, relatives and doctors. The MRI scan will be performed at St Thomas' Hospital, KCL, taking about one hour with a comfort break halfway through. Attention will be paid to ensure the mother is comfortable during the scan and can talk with the imaging team throughout. We will perform an ultrasound scan during the same visit. Parents will be shown images of their baby and copies provided. If there are any incidental findings on the imaging that are known to influence the management of the birth or delivery, such as a low lying placenta, these will be discussed with the mother and shared with the obstetric and midwifery team. We will collect clinical data acquired as part of routine antenatal care. with information from the labour and delivery and the mothers and baby's outcome. Six weeks after birth we will contact and ask mothers for information about their physical and mental wellbeing, feeding method and baby's health. With all available information for an individual pregnancy we will use the latest data modelling tools to determine which factors relate to a higher chance of a difficult delivery. This should provide the most valuable data required to produce an individual risk score that can be used prospectively in women at high risk for EmCS or assisted delivery. Our ultimate aim is to reduce the numbers of EmCS and assisted deliveries, thereby improving the short and long-term health and wellbeing of both mothers and babies.

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Researchers

Andrew Melbourne (Co-Investigator)Anna David (Co-Investigator)Dimitrios Siassakos (Co-Investigator)Jana Hutter (Co-Investigator)Manju Chandiramani (Co-Investigator)Maria Deprez (Co-Investigator)Mary Ann Rutherford (Principal Investigator)Neil Oxtoby (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise birth management. The MiBIRTH study.
Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise BIRTH management.
Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise BIRTH management. The MiBIRTH study
Individualised risk prediction of fetal infection and development in pregnancies that deliver preterm using advanced MRI techniques and machine learning
Magnetic resonance imaging to enhance the diagnosis of fetal developmental brain abnormalities in utero

Original classification

Research Grant

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