Gestational Diabetes Monitoring and Management

University of Oxford

Status

Active, not recruiting

Conditions

Gestational Mother

Pregnancy Weight Gain

Pregnancy Induced Hypertension

Gestational Diabetes

Gestational Diabetes Mellitus in Pregnancy

Pregnancy Loss

Pregnancy in Diabetic

Pregnancy Bleeding

Birth, Preterm

Birth Outcome, Adverse

Birth Hypoxia

Birth Weight

Pregnancy Complications

Diabetes Complications

Gestational Hypertension

Pregnancy Preterm

Gestational Weight Gain

Gestational Complication

Pregnancy, High Risk

Study type

Observational

Funder types

Other

Identifiers

NCT06963528

301255_Minor Amendment 5

IRAS 301255 (Other Identifier)

Details and patient eligibility

About

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.

Full 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.

Enrollment

1,800 patients

Sex

Female

Ages

18 to 99 years old

Volunteers

No Healthy Volunteers

Inclusion criteria