Air Pollution and Pregnancy (PTB)

November 21, 2025 updated by: Queen Mary University of London

The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Children born prematurely have higher rates of cerebral palsy, sensory deficits, learning disabilities and respiratory illness. In the UK, approximately 60,000 babies are born prematurely each year. This is equivalent to 1 in 9 pregnancies in England and the numbers increase to 1 in every 7 pregnancies in London. In around 40% of cases, the cause of preterm birth are unknown. Current algorithms to predict preterm birth are limited in their ability to identify women at highest risk of delivering preterm and do not consider genetic, lifestyle and environmental circumstances within their prediction. With the rapid development of machine learning and deep learning, it is now possible to develop models which can consider a higher number of variables within their predictive algorithm, to formulate a patient specific prediction of risk. There is growing evidence that maternal exposure to air pollution during pregnancy is associated with an increased risk of preterm birth. Exposure to air pollution may be associated with poor placental function, pre-eclampsia, and poor fetal growth although there is limited data on these adverse pregnancy outcomes, all of which can lead to preterm birth. At present, many of the recent epidemiological studies in this area lack detailed and matching clinical data sets without gaps in electronic records.

This study aims to:

  1. Link data on air pollution exposure with highly detailed clinical data sets extracted from patient electronic health records from University College London Hospital NHS Trust (UCLH)
  2. Develop a computational model which can accurately predict the gestation at which a patient will deliver in weeks and days
  3. Using the model, identify the timepoints in pregnancy that air pollution has the greatest impact on pregnancy outcomes

Study Type

Observational

Enrollment (Estimated)

200000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Machine learning model to predict the risk of preterm birth and adverse birth outcomes
Time Frame: 36 months
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Machine learning model to predict how air quality increases the risk of preterm birth and adverse birth outcomes
Time Frame: 42 months
This model will also review the temporal sequence of events within a patient's medical history and current pregnancy, identifying significant interactions. Other adverse pregnancy outcomes such as birthweight, birthweight centile, pre-eclampsia, small for gestational age, fetal growth restriction will also be studied.
42 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Queen Mary University of London

Collaborators

University College London Hospitals

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 1, 2024

Primary Completion (Estimated)

October 31, 2029

Study Completion (Estimated)

November 30, 2029

Study Registration Dates

First Submitted

March 26, 2024

First Submitted That Met QC Criteria

March 26, 2024

First Posted (Actual)

April 2, 2024

Study Record Updates

Last Update Posted (Actual)

November 24, 2025

Last Update Submitted That Met QC Criteria

November 21, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 331247

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Data from the EPIC electronic health records database will be anonymized at UCLH to create a secondary dataset with anonymized identifier for patient identifier, postcode (area) and delivery date (month). Raw data screened. Patients excluded according to criteria.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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