Ignite Creation Date:
2025-12-24 @ 9:24 PM
Ignite Modification Date:
2025-12-30 @ 9:42 PM
Study NCT ID:
NCT05164432
Status:
UNKNOWN
Last Update Posted:
2023-10-26
First Post:
2021-11-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Prediction of Adverse Outcome Using Fetal MRI in Pregnancies at Risk of Preterm Birth
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D008279', 'term': 'Magnetic Resonance Imaging'}], 'ancestors': [{'id': 'D014054', 'term': 'Tomography'}, {'id': 'D003952', 'term': 'Diagnostic Imaging'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Two groups of women will be recruited those at high risk of preterm birth and those who have uncomplicated pregnancies'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 175}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-12-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-10', 'completionDateStruct': {'date': '2025-11-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-10-25', 'studyFirstSubmitDate': '2021-11-15', 'studyFirstSubmitQcDate': '2021-12-06', 'lastUpdatePostDateStruct': {'date': '2023-10-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-12-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-11-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number of participants with chorioamnionitis', 'timeFrame': 'After delivery (within approximately three months of recruitment depending on the gestation at delivery)', 'description': 'Chorioamnionitis will be diagnosed on placental histology'}, {'measure': 'Neonatal morbidity', 'timeFrame': 'Neonatal period post delivery (up to approximately seven months after recruitment depending on the gestation at delivery)', 'description': 'A composite neonatal adverse outcome will be created'}], 'secondaryOutcomes': [{'measure': 'Individual adverse neonatal outcomes', 'timeFrame': 'Neonatal period post delivery (up to approximately seven months after recruitment depending on the gestation at delivery)', 'description': 'Specific neurological, respiratory, GI and individual systems adverse outcomes for the neonate'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Preterm Birth Complication']}, 'descriptionModule': {'briefSummary': "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.\n\nEarly 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.\n\nUsing 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.\n\n137 pregnant women at high risk of PTB (between 16-32 weeks of pregnancy) and 183 women with uncomplicated pregnancies will be invited to participate.\n\nWomen 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).\n\nAfter 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.", 'detailedDescription': "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.\n\nInfection 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.\n\nFetal 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.\n\nMRI 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.\n\nAims 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.\n\nStudy 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.\n\nWomen will be identified as high-risk if:\n\n1. 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.\n2. they have lost the fluid (waters) around the baby\n3. 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).\n\nAfter 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.\n\nAll 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.\n\nIf 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."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'minimumAge': '16 Years', 'genderBased': True, 'genderDescription': 'Pregnant women will be recruited', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* pregnant women with uncomplicated pregnancies 16-42 weeks pregnant OR\n* pregnant women at high risk of preterm birth before 32 weeks gestation\n\nExclusion Criteria:\n\n* inability to give informed consent\n* multiple pregnancy\n* gestational diabetes\n* pre-eclampsia\n* fetuses known to have chromosomal or fetal abnormalities\n* a recently sited maternal metallic implant, claustrophobia.'}, 'identificationModule': {'nctId': 'NCT05164432', 'briefTitle': 'Prediction of Adverse Outcome Using Fetal MRI in Pregnancies at Risk of Preterm Birth', 'organization': {'class': 'OTHER', 'fullName': "King's College London"}, 'officialTitle': 'Individualised Risk Prediction of Adverse Neonatal Outcome in Pregnancies That Deliver Preterm Using Advanced MRI Techniques and Machine Learning', 'orgStudyIdInfo': {'id': '1121988'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'High risk of preterm birth', 'description': 'Women at high risk of preterm birth', 'interventionNames': ['Diagnostic Test: MRI scan']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Women with low risk pregnancies', 'description': 'Women with uncomplicated pregnancies anticipated to deliver at term', 'interventionNames': ['Diagnostic Test: MRI scan']}], 'interventions': [{'name': 'MRI scan', 'type': 'DIAGNOSTIC_TEST', 'description': 'Women will have an MRI scan during pregnancy to evaluate the fetus and placenta', 'armGroupLabels': ['High risk of preterm birth', 'Women with low risk pregnancies']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'SE1 7EH', 'city': 'London', 'state': 'Greater London', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'Lisa Story, MD PhD', 'role': 'CONTACT', 'email': 'lisa.story@kcl.ac.uk', 'phone': '020 7188 7083'}, {'name': 'Jana Hutter, PhD', 'role': 'CONTACT', 'email': 'jana.hutter@kcl.ac.uk', 'phone': '020 7188 7083'}, {'name': 'Lisa Story, MD PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': "St Thomas' Hospital, King's College London", 'geoPoint': {'lat': 51.50853, 'lon': -0.12574}}], 'centralContacts': [{'name': 'Lisa Story, MD PhD', 'role': 'CONTACT', 'email': 'lisa.story@kcl.ac.uk', 'phone': '020 7188 7083'}, {'name': 'Reza Razavi, MD PhD', 'role': 'CONTACT', 'email': 'reza.razavi@kcl.ac.uk', 'phone': '02078483224'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'CSR', 'ANALYTIC_CODE'], 'timeFrame': 'Data is suitable for sharing in anonymised form. It is intended that the data will be made available on completion of data collection and publication of findings.', 'ipdSharing': 'YES', 'description': 'Anonymised data will be shared with other researchers', 'accessCriteria': 'Data can be used for academic purposes. Users are bound by a data sharing agreement which does not allow the commercial use of the data and sets out their responsibilities to acknowledge publications and funding associated with the data, not attempting to de-anonymise and identify any subjects as well as their duty to inform the research team of any relevant observations and results.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "King's College London", 'class': 'OTHER'}, 'collaborators': [{'name': 'National Institute for Health Research, United Kingdom', 'class': 'OTHER_GOV'}, {'name': "Guy's and St Thomas' NHS Foundation Trust", 'class': 'OTHER'}, {'name': 'Massachusetts Institute of Technology', 'class': 'OTHER'}, {'name': "Boston Children's Hospital", 'class': 'OTHER'}, {'name': "Phoenix Children's Hospital", 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}