Air Pollution and Pregnancy
Recruiting now
Conditions studied: Premature Birth
In brief
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 facts
- Study ID
- NCT06340971
- Run by
- Queen Mary University of London
- People needed
- 200000
- Starts
- 2024-11-01
- Expected to finish
- 2029-11-30
- Last updated by the study team
- 2025-11-24
Who can join
Age: 18 and older. Sex: female. Healthy volunteers: not accepted.
You may qualify if…
- We aim to include data from pregnant women who delivered at University College London Hospitals from 2019 onwards after the start of the EPIC electronic patient record. The is no specified age range for this study, so as to improve inclusivity. We also aim to represent minority ethnic groups and patients with social deprivation within our dataset.
You may not qualify if…
- We will exclude data from patients with an incomplete duration of follow-up due to transfer of antenatal care for delivery at another trust. Patients with incomplete past obstetric history data, inaccurate estimations of gestational age (e.g. due to late booking of the pregnancy) and missing data for 'postcode of usual address' will also be excluded. Patients who are less than 18 years of age will be excluded.
Where it is running
- Tina Chowdhury — London, United Kingdom (enrolling)
- Anna David — London, United Kingdom (enrolling)
Full record on ClinicalTrials.gov
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