Air Pollution and Pregnancy

Sponsor
Queen Mary University of London
Study ID
NCT06340971
Status
Recruiting
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Conditions

  • Premature Birth

Eligibility Criteria

Sex
FEMALE
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Policy — OTHER
    We will work with stakeholders' policy groups e.g. RCOG, RCM, RCP and policy makers e.g. Department for Health and Social Care, Transport Emissions at the Greater London Authority or Mayor of London's office to disseminate our findings and develop public health messages. We aim to develop guidance on how pregnant women and their families can reduce their exposure to air pollution by highlighting for example travel routes with less pollution and wear face masks.

Study Details

We are an inter-disciplinary team of UK scientists with expertise in obstetrics, women's and child health, epidemiology, climate science, inflammation, computational modelling, machine learning and artificial intelligence. Together we have a long history with existing strengths underlying preterm birth research that crosses multiple disciplines and an excellent track record of publications and awards leading research in preterm birth. We aim to develop and validate a deep learning model to predict the risk of preterm birth and other adverse pregnancy outcomes using data from EPIC electronic health records at University College London Hospital Trust (UCLH) for a cohort of 18000 patients. We will obtain corresponding data on exposure to ambient pollution using non-identifiers for postcode (area) and date of delivery (month). The model will review the temporal sequence of events within a patient's medical history and current pregnancy, identifying significant interactions and will predict the risk of preterm birth. It will also determine the threshold and gestation at which pollution exposure has the greatest impact.

Key Dates

First listed
Apr 2, 2024
Start date
Nov 1, 2024
Status verified
Nov 2025
Primary completion
Oct 31, 2029
Completion
Nov 30, 2029

Study Design

Enrollment
200,000 participants (estimated)

Primary Outcome Measure

Machine learning model to predict the risk of preterm birth and adverse birth outcomes [ Time Frame: 36 months ]

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