Air Pollution and Pregnancy

NCT06340971 | Recruiting DMC

• 1 other identifier: 331247
• Study Type: observational
• Target: 200,000
• Locations: 1 country
• Sites: 2

Brief Summary

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

Premature Birth

Keywords

Preterm birth; Pregnancy; Premature birth; Air pollution; Air quality

Outcome Measures

Primary Outcomes (1)

• Machine learning model to predict the risk of preterm birth and adverse birth outcomes

  We aim to develop a deep learning algorithm to predict the risk of preterm birth and other adverse pregnancy outcomes using data from electronic health records and a spatiotemporal model for ambient pollution levels within London. The model will consider personal, lifestyle and environmental factors alongside traditional risk factors to predict the gestation of pregnancy that delivery is most likely to occur. This can be classified as 'term', 'late preterm', 'moderate preterm', 'very preterm' and 'extreme preterm'.

  36 months

Secondary Outcomes (1)

• Machine learning model to predict how air quality increases the risk of preterm birth and adverse birth outcomes

  42 months

Interventions

Policy OTHER

We will work with stakeholders' policy groups e.g. RCOG, RCM, RCP and policy makers e.g. Department for Health and Social Care, Transport Emissions at the Greater London Authority or Mayor of London's office to disseminate our findings and develop public health messages. We aim to develop guidance on how pregnant women and their families can reduce their exposure to air pollution by highlighting for example travel routes with less pollution and wear face masks.

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: Pregnant women over the age of 18 years old
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

We aim to include data from pregnant women who delivered at UCLH from 2019 when EPIC was launched and until the end of 2023. There is no specified upper age range for this study. To improve inclusivity, we will aim to collect information from all women booking and delivering at UCLH to ensure minority ethnic groups and patients with social deprivation or with additional pregnancy complicating disorders are included within our dataset.

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.

Sponsors & Collaborators

• Queen Mary University of London: lead
• University College London Hospitals: collaborator

Study Sites (2)

Tina Chowdhury

London, E14NS, United Kingdom

RECRUITING

Anna David

London, NW1 2PG, United Kingdom

RECRUITING

MeSH Terms

Conditions

Premature Birth

Interventions

Policy

Condition Hierarchy (Ancestors)

Obstetric Labor, Premature; Obstetric Labor Complications; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases

Intervention Hierarchy (Ancestors)

Health Care Economics and Organizations

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 26, 2024
• First Posted: April 2, 2024
• Study Start: November 1, 2024
• Primary Completion (Estimated): October 31, 2029
• Study Completion (Estimated): November 30, 2029
• Last Updated: November 24, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

GB (2)