Premature BirthPTBNCT06340971
Air Pollution and Pregnancy
An observational study of Premature Birth, run by Queen Mary University of London at 2 sites in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Premature Birth
- What is being tested?
- Policy
- How many people
- 200,000 participants
- Where
- 2 sitesUnited Kingdom
- Study dates
- 1 Nov 2024to 30 Nov 2029
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom2 sites, 2 recruiting
- Recruiting
Tina Chowdhury
London, E14NS
Tina Chowdhury, 0207882 ext. 7560, t.t.chowdhury@qmul.ac.uk
Rhona Atkins, 0207882 ext. 7272, rhona.atkin@nhs.net
- Recruiting
Anna David
London, NW1 2PG
Yaa Acheampong, 020 3447 ext. 6164, y.acheampong@nhs.net
Anna David, 0203 447 ext. 9400, a.david@nhs.net
About this study
Official title: 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.
Read the 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:
- 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)
- Develop a computational model which can accurately predict the gestation at which a patient will deliver in weeks and days
- Using the model, identify the timepoints in pregnancy that air pollution has the greatest impact on pregnancy outcomes
- Conditions
- Premature Birth
- Being tested
- Policy
- Keywords
- Preterm birth, Pregnancy, Premature birth, Air pollution, Air quality
- Medical subject headings
- Premature Birth
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PolicyOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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'.
Measured: 36 months
Secondary outcomes (1)
Machine learning model to predict how air quality increases the risk of preterm birth and adverse birth outcomes
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.
Measured: 42 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2024
- Study start1 Nov 2024
- Primary completion31 Oct 2029 (estimated)
- Study completion30 Nov 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Premature BirthStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 200,000 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Queen Mary University of London
- Sponsor type
- University, hospital or other
- Collaborators
- University College London Hospitals
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06340971
- Sponsor's protocol ID
- 331247
- Acronym
- PTB
- Status last verified
- Nov 2025
- First submitted
- 26 Mar 2024
- Last updated
- 24 Nov 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 24 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes
- ClinicalTrials.gov ID
- NCT06340971
- Acronym / sponsor ID
- PTB / 331247
- Conditions
- Premature Birth
- Keywords
- Preterm birth, Pregnancy, Premature birth, Air pollution, Air quality
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200,000 participants (estimated)
- Lead sponsor
- Queen Mary University of LondonUniversity, hospital or other
- Collaborators
- University College London Hospitals
- Study start
- 1 Nov 2024 (actual)
- Primary completion
- 31 Oct 2029 (estimated)
- Study completion
- 30 Nov 2029 (estimated)
- First posted
- 2 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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:
- 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)
- Develop a computational model which can accurately predict the gestation at which a patient will deliver in weeks and days
- Using the model, identify the timepoints in pregnancy that air pollution has the greatest impact on pregnancy outcomes
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PolicyWe 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. | Other | – |
Primary outcome
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'.
1 secondary outcome
Machine learning model to predict how air quality increases the risk of preterm birth and adverse birth outcomes
Time frame: 42 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Nov 2025.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Premature Birth, run by Queen Mary University of London at 2 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine learning model to predict the risk of preterm birth and adverse birth outcomes (time frame: 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Nov 2025.