MedPath

Fetal sensor for placental monitoring to detect maternal, fetal and neonatal outcome

Not Applicable
Conditions
Health Condition 1: O098- Supervision of other high risk pregnancies
Registration Number
CTRI/2023/11/060228
Lead Sponsor
niversity College of London
Brief Summary

Not available

Detailed Description

Not available

Recruitment & Eligibility

Status
ot Yet Recruiting
Sex
Not specified
Target Recruitment
0
Inclusion Criteria

Pregnant women with fetal gestation of 28 weeks or above with high-risk pregnancy (pre- eclampsia (PE) / pre-existing or gestational diabetes mellitus (GDM) and reactive hypoglycemia (a condition with outcomes similar to GDM according to UCLH research) / Small for Gestational Age (SGA) / Fetal Growth Restriction (FGR) and postdates ( >40 weeks)

Exclusion Criteria

1.Fetal malformation

2.Fetal genetic condition

3.Multiple gestation

4.Participants unable to read and respond to questionnaires in English or Hindi (India)

Study & Design

Study Type
Observational
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Assess the relationship between optical markers of oxygenation and metabolism with fetoplacental compromise and outcome. Poor outcomes are defined as Small for Gestational Age or fetal growth restriction, stillbirth or poor condition of the newborn infants at birth while good outcomes indicate live birth of a healthy appropriately grown newborn infant. <br/ ><br>Development of an optical biomarker of outcome combining placental oxygenation, metabolism, fetal heart rate and fetal movement and establishing an early warning system for risk identification <br/ ><br>Identify whether placental function (oxygenation and metabolism) differs between high income countries (UCLH) and LMIC (AIIMS) and relate to the difference in the neonatal outcome <br/ ><br>Timepoint: six months
Secondary Outcome Measures
NameTimeMethod
Investigate the associations between the optical markers of placental oxygenation and metabolism with placental growth factor, placental histological features associated with stillbirth. We will integrate placental histological features associated with stillbirth in the AI modelling to better recognize in-utero features of a placental function that correlate with postnatal pathology <br/ ><br>Investigate RBC deformability in the umbilical vein and how it correlates with metabolic patterns and with the NIRS findings <br/ ><br>Timepoint: six months
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