Description Module

Description Module

The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.

Description Module path is as follows:

Study -> Protocol Section -> Description Module

Description Module


Ignite Creation Date: 2025-12-26 @ 10:32 AM
Ignite Modification Date: 2025-12-26 @ 10:32 AM
NCT ID: NCT06963528
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.
Study: NCT06963528
Study Brief:
Protocol Section: NCT06963528