Understanding the impact of air pollution on premature birth
Official title: The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes
Run by Queen Mary University of London · NCT06340971 · Data updated 2026-11-12
This study is trying to see if air pollution affects the chances of having a premature baby by looking at health records from 18,000 patients.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | Women only |
| Enrollment | 200000 (estimated) |
| Sponsor | Queen Mary University of London Academic / other |
| Locations | 2 sites (London and 1 other locations) |
| Status | Recruiting |
| Started | November 1, 2024 |
| Main results expected | October 31, 2029 |
| Study ends | November 30, 2029 |
| Registry record updated | November 24, 2025 |
| Trial ID | NCT06340971 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are pregnant women who delivered at University College London Hospitals from 2019 onwards.
✕Probably not a fit
Patients with incomplete medical records or those who delivered at other trusts will not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study aims to develop and validate a deep learning model to predict the risk of preterm birth and other adverse pregnancy outcomes by analyzing data from 18,000 patients at University College London Hospital Trust. The research team, composed of experts in various fields, will link electronic health records with data on air pollution exposure to identify significant interactions affecting pregnancy outcomes. By utilizing advanced machine learning techniques, the study seeks to improve the accuracy of preterm birth predictions, which currently lack comprehensive clinical data.
Why it matters
Potential benefit: If successful, this study could lead to better prediction and prevention strategies for preterm birth, ultimately improving maternal and neonatal health outcomes.
How similar studies have gone: Other studies have shown promise in using machine learning for predicting pregnancy outcomes, but this specific approach linking air pollution data is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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. Exclusion Criteria: * 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's running
-
Tina Chowdhury
London, United Kingdom Recruiting -
Anna David
London, United Kingdom Recruiting
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.