Prediction of Adverse Outcome Using Fetal MRI in Pregnancies at Risk of Preterm Birth

NCT ID: NCT05164432

Last Updated: 2023-10-26

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 175 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-12-01
• Study Completion Date: 2025-11-01

Brief Summary

1.4% of babies have 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. This has significant cost implications for the NHS, education services and immeasurable human costs for the child and their family.

Early delivery may result from maternal infection or poor attachment of the placenta to the womb, which may also cause abnormal brain and lung development. Even where obvious signs of infection are not present in the mother, subtle infection is often present in the baby. Currently there is no test routinely used to see if there is an infection of the baby inside the womb, and it is unknown how the placenta develops in babies that subsequently deliver preterm.

Using MRI, the investigators will assess the baby's thymus and placenta for signs of infection and assess how the lungs and brain are developing whilst still in the womb. Machine learning techniques, where computers analyze all the results together, will then be used to see if these scans can identify babies that do poorly after birth.

137 pregnant women at high risk of PTB (between 16-32 weeks of pregnancy) and 183 women with uncomplicated pregnancies will be invited to participate.

Women will have an MRI scan of the fetus assessing the lung, brain, thymus and placenta. Where high risk women do not deliver, repeat imaging will be offered every two weeks (maximum 3).

After birth the investigators will see if infection was present by analysing the placenta under a microscope, and see how the baby does. All the information from scans and after birth will be put into a computer, to predict which babies do poorly after birth. Health records of the child will be accessed up to two years of age.

Detailed Description

1.4% of babies have 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 early delivery and subsequent abnormal brain and lung development. Where obvious signs of infection are not present in the mother, subtle infection is often present in the unborn baby. Currently there is no test routinely used to see if there is an infection of the baby in the womb.

Fetal Magnetic Resonance Imaging (MRI), already used in clinical practice, can produce very clear images of the baby's brain and lungs. It can also assess blood flow and structure in detail, as well as overall size. The investigators already have data, which shows areas of the brain and lungs are smaller in babies that subsequently deliver very preterm, indicating factors that drive 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 an organ in the neck (the thymus) vital to the baby's immune system and by scanning the placenta. The investigators have new data that suggests that these methods could help pick up infection. Women who have previously undergone preterm birth have helped shape this study.

Aims The investigators want to ascertain if it is possible to predict which babies are likely to develop serious complications after preterm birth. Using MRI, the thymus and placenta will be assessed for signs of infection and how the lungs and brain are developing whilst still in the womb will be monitored. The investigators will evaluate if these scans accurately identify the babies that 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:

1. they have no symptoms but are, attending the Preterm Surveillance Clinic at St Thomas's Hospital, with risk factors for PTB (a previous premature delivery or surgery to the neck of the womb (cervix)) and are likely to deliver early, which we can pick up by seeing if the cervix has already shortened, and by doing a vaginal swab test.

2. they have lost the fluid (waters) around the baby

3. the cervix has opened before 24 weeks These women will have an MRI scan of the fetal lung, brain, thymus and placenta. Repeat imaging will be offered every two weeks (maximum of three scans).

After birth the investigators will see if infection is present by analysing the umbilical cord blood and looking at the placenta under a microscope. Information about complications the babies have until they leave hospital will be collected.

All of this information, as well as from scans from healthy pregnant women involved in other research studies both in this country and from abroad, will be combined in a new test, using 'machine learning', which involves computers analysing the data to see if babies most likely to have problems after birth can be identified. Results will be presented in scientific papers, at conferences and through social media.

If the test works, the next step would be to find out when the best time is to deliver the baby; this may be sooner if the fetus is known to have an infection. The appropriate timing of existing treatments to prevent brain and lung injury may also be facilitated with more studies in the future.

Conditions

Preterm Birth Complication

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: NONE

Study Groups

High risk of preterm birth

Women at high risk of preterm birth

Group Type: EXPERIMENTAL

MRI scan

Intervention Type: DIAGNOSTIC_TEST

Women will have an MRI scan during pregnancy to evaluate the fetus and placenta

Women with low risk pregnancies

Women with uncomplicated pregnancies anticipated to deliver at term

Group Type: ACTIVE_COMPARATOR

MRI scan

Intervention Type: DIAGNOSTIC_TEST

Women will have an MRI scan during pregnancy to evaluate the fetus and placenta

Interventions

MRI scan

Women will have an MRI scan during pregnancy to evaluate the fetus and placenta

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• pregnant women with uncomplicated pregnancies 16-42 weeks pregnant OR
• pregnant women at high risk of preterm birth before 32 weeks gestation

Exclusion Criteria

• inability to give informed consent
• multiple pregnancy
• gestational diabetes
• pre-eclampsia
• fetuses known to have chromosomal or fetal abnormalities
• a recently sited maternal metallic implant, claustrophobia.

• Minimum Eligible Age: 16 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

National Institute for Health Research, United Kingdom

OTHER_GOV

Sponsor Role: collaborator

Guy's and St Thomas' NHS Foundation Trust

OTHER

Sponsor Role: collaborator

Massachusetts Institute of Technology

OTHER

Sponsor Role: collaborator

Boston Children's Hospital

OTHER

Sponsor Role: collaborator

Phoenix Children's Hospital

OTHER

Sponsor Role: collaborator

King's College London

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

St Thomas' Hospital, King's College London

London, Greater London, United Kingdom

Site Status: RECRUITING

Countries

United Kingdom

Central Contacts

Lisa Story, MD PhD

Role: CONTACT

lisa.story@kcl.ac.uk

020 7188 7083

Reza Razavi, MD PhD

Role: CONTACT

reza.razavi@kcl.ac.uk

02078483224

Facility Contacts

Lisa Story, MD PhD

Role: primary

lisa.story@kcl.ac.uk

020 7188 7083

Jana Hutter, PhD

Role: backup

jana.hutter@kcl.ac.uk

020 7188 7083

Other Identifiers

1121988

Identifier Type: -

Identifier Source: org_study_id