Understanding the impact of air pollution on premature birth
The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes
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 |
|---|---|
| Enrollment | 200000 (estimated) |
| Ages | 18 Years and up |
| Sex | Female |
| Sponsor | Queen Mary University of London Academic / other |
| Locations | 2 sites (London and 1 other locations) |
| Trial ID | NCT06340971 on ClinicalTrials.gov |
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.
Who should consider this trial
Good fit: Ideal candidates for this study are pregnant women who delivered at University College London Hospitals from 2019 onwards.
Not a fit: Patients with incomplete medical records or those who delivered at other trusts will not benefit from this study.
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 performed: Other studies have shown promise in using machine learning for predicting pregnancy outcomes, but this specific approach linking air pollution data is relatively novel.
Eligibility criteria
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 this trial is running
London and 1 other locations
- Tina Chowdhury — London, United Kingdom (Recruiting)
- Anna David — London, United Kingdom (Recruiting)
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.