Clinical trial NCT06963528: Gestational Diabetes Monitoring and Management
Official title: Predictive Monitoring and Management of Pregnant Women With Gestational Diabetes Mellitus
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Gestational Diabetes
Pregnancy Complications
Pregnancy in Diabetic
Pregnancy, High Risk
Diabetes Complications
Pregnancy Induced Hypertension
Pregnancy Weight Gain
Gestational Hypertension
Gestational Weight Gain
Gestational Diabetes Mellitus in Pregnancy
Gestational Complication
Gestational Mother
Pregnancy Preterm
Pregnancy Bleeding
Pregnancy Loss
Birth Weight
Birth Outcome, Adverse
Birth, Preterm
Birth Hypoxia
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
Lead sponsor: University of Oxford
Sponsor class: OTHER
Collaborators: name: Royal Academy of Engineering
name: Oxford University Hospitals NHS Trust
First posted: 2025-05-09
Enrollment: 1800
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Routinely collected from the NHS and submerged into four groups, including Caucasian, Asian, Black, and other

Brief 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. 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.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Clinical outcome of mothers at birth
timeFrame: From enrollment to the delivery date, assesed upto 52 weeks.
measure: Clinical outcome of mothers after birth
timeFrame: From the delivery date to the date that mother was discharged from the hospital, assessed up to 52 weeks, whichever come first.
measure: Clinical outcome of neonates at birth
timeFrame: From enrollment to the delivery date to the date to giving birth, assesed upto 52 weeks.
measure: Clinical outcome of neonates after birth
timeFrame: From the delivery date to the date that neonate was discharged from the hospital, assessed up to 52 weeks, whichever come first.
Secondary outcomes:
Other outcomes:
Locations:
city: Oxford
country: United Kingdom
facility: University of Oxford
Study officials:
affiliation: University of Oxford
name: Huiiq Yvonne Lu
role: PRINCIPAL_INVESTIGATOR
References: