Air Pollution and Pregnancy

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.

NCT Number	NCT06340971
Lead Sponsor	Queen Mary University of London Collaborators: University College London Hospitals
Conditions	Premature Birth
Enrollment	200,000 participants
Start Date	2024-11-01
Primary Completion	2029-10-31 (estimated)
Study Completion	2029-11-30 (estimated)
Updated on ClinicalTrials.gov	2025-11-24