Actively Recruiting
Air Pollution and Pregnancy
Led by Queen Mary University of London · Updated on 2025-11-24
200000
Participants Needed
2
Research Sites
265 weeks
Total Duration
On this page
Sponsors
Q
Queen Mary University of London
Lead Sponsor
U
University College London Hospitals
Collaborating Sponsor
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Air Pollution and Pregnancy
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Pregnant women who delivered at University College London Hospitals from 2019 onward
- No specified age range to improve inclusivity
- Representation of minority ethnic groups and patients with social deprivation within the dataset
You will not qualify if you...
- Patients younger than 18 years of age
- Patients with incomplete follow-up due to transfer of antenatal care for delivery at another trust
- Patients with incomplete past obstetric history data
- Patients with inaccurate estimations of gestational age (e.g., late booking of pregnancy)
- Patients missing data for postcode of usual address
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 2 locations
1
Tina Chowdhury
London, United Kingdom, E14NS
Actively Recruiting
2
Anna David
London, United Kingdom, NW1 2PG
Actively Recruiting
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
Not the Right Trial for You?
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here