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Gestational DiabetesNCT06963528

Gestational Diabetes Monitoring and Management

An observational study of Gestational Diabetes and Pregnancy Complications, run by University of Oxford at 1 site in United Kingdom.

Active, not recruiting

Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record

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Trial summary

The primary goal is to predict the clinical outcomes of mother and baby using blood glucose and other routinely collected clinical data in pregnancy to predict adverse outcomes at birth in women with GDM. The secondary goal is to develop models to predict optimal blood glucose testing schedules for pregnant women. Read more about this study

Who is it for?
Ages 18 to 99 years, women only
How many people
1,800 participants
Where
1 siteUnited Kingdom
Study dates
1 Dec 2021to 31 Aug 2025
Phase
Observational

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 to 99 years
  • Women only
  • No healthy volunteers
  • Adult, Older adult

Study population: Routinely collected from the NHS and submerged into four groups, including Caucasian, Asian, Black, and other

Who may be able to join

  • Pregnant women with GDM during pregnancy
  • Record of blood glucose monitoring registered on the GDm-Health system

Who cannot join

  • The participant may not enter the study if ANY of the following apply:
  • Women who have not consented for their data to be shared through GDm-Health
  • Women who opted out of the use of their data in health research

Trial locations and contacts

1 site in 1 country.

United Kingdom1 site
  • University of Oxford

    Oxford, OX1 3PJ

Other trials in United Kingdom

About this study

Official title: Predictive Monitoring and Management of Pregnant Women With Gestational Diabetes Mellitus

The primary goal is to predict the clinical outcomes of mother and baby using blood glucose and other routinely collected clinical data in pregnancy to predict adverse outcomes at birth in women with GDM. The secondary goal is to develop models to predict optimal blood glucose testing schedules for pregnant women. Exploratory Objectives are (1) to understand patterns of dosage and / or medication choice and (2) to describe different phenotypes of gestational diabetes based on multiple data input.

Read the detailed description

Gestational diabetes is a sub-type of diabetes that causes a person's blood sugar level to become too high during pregnancy. This health condition affects approximately 10% of pregnant women in the UK and up to 20% worldwide. Women who have gestational diabetes need to take daily blood tests to monitor their blood sugar. While much work exists on telehealth using blood glucose monitoring, little exists in modern AI-based methods for performing the prediction of patient health status in such settings. This study builds on world-leading research in this field within the Institute of Biomedical Engineering and the Nuffield Department of Women's \& Reproductive Health at the University of Oxford. The focus of this project is to clearly identify patients in different risk groups, predict the clinical outcome of mothers and babies, and reduce the overall number of blood tests. During this study, CI and investigators will develop novel state-of-the-art AI models to improve blood glucose control. This study will use existing retrospective data in pursuit of objectives. The hypothesis in this study is that better blood glucose control will improve clinical outcomes. The predictive models developed in this research study will provide an estimate of patient-specific health risk through time, and notify patients of the clinically appropriate number of blood glucose tests required to monitor their condition. As a result, innovations arising from this study can support future studies to facilitate rapid clinical treatment, transform a hospital-only treatment pathway into a cost-effective home-based alternative, and improve the overall quality of maternal healthcare.

Keywords
gestational diabetes, machine learning, predictive monitoring, clustering, patient subtype, deep learning, transfomer model, foundation model, diabetes, blood glucose, c section, high risk pregnancy, diabetes complications
Medical subject headings
Diabetes, Gestational, Pregnancy Complications, Pregnancy in Diabetics, Diabetes Complications, Hypertension, Pregnancy-Induced, Gestational Weight Gain, Birth Weight, Premature Birth

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT06963528
Study groupRoleWhat they receive
Mothers with diabetes in pregnancyMothers with first appearance of diabetes in pregnancy

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Clinical outcome of mothers at birth

    Gestational age at delivery in years, Mode of delivery (vaginal, caesarean, assisted) as categocial measurements, Maternal weight gain in kg, Maternal pregnancy-induced hypertension in binary Yes or No, Maternal pregnancy-induced pre-eclampsia in binary Yes or No.

    Measured: From enrollment to the delivery date, assesed upto 52 weeks.

  2. Clinical outcome of mothers after birth

    Perineal trauma (3rd- or 4th-degree perineal tear or tear requiring suturing in the operating room) in text notes, Admission to higher level of care for mother in ICD codes, Length of hospital stay for mother in days, Method of feeding at discharge from hospital in texts.

    Measured: From the delivery date to the date that mother was discharged from the hospital, assessed up to 52 weeks, whichever come first.

  3. Clinical outcome of neonates at birth

    Newborn status at birth in categocial (alive, stillbirth), Birth weight in kg, Gender of neonate in categocial of male or female, APGAR score at 5 mins in numerical numbers from 0 to 10.

    Measured: From enrollment to the delivery date to the date to giving birth, assesed upto 52 weeks.

  4. Clinical outcome of neonates after birth

    Incidence of shoulder dystocia/birth injury in ICD codes to indicate yes or no, Incidence of neonatal hypoglycaemia in ICD codes to indicate Yes or No, Incidence of neonatal significant hyperbilirubinemia in ICD codes to indicate Yes or No, Length of hospital stay for neonate in days.

    Measured: From the delivery date to the date that neonate was discharged from the hospital, assessed up to 52 weeks, whichever come first.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted9 May 2025
  2. Study start1 Dec 2021
  3. Primary completion1 Sep 2023
  4. Study completion31 Aug 2025 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Retrospective
Enrolment
1,800 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06963528
Sponsor's protocol ID
301255_Minor Amendment 5
Other IDs
IRAS 301255
Status last verified
Dec 2024
First submitted
26 Mar 2025
Last updated
18 May 2025
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No: Ethical approval restrictions.

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