Air Pollution and Pregnancy

ConditionPremature Birth

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexFemale

Age18+

SponsorQueen Mary University of London

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

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 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.

Trial design

Design model

Cohort

Time perspective

Other

Treatments tested in this trial

Policy
Other intervention
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.

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

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'.

Time frame

36 months

Secondary outcomes

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.

Time frame

42 months

Sponsors and contacts

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Queen Mary University of London

Lead sponsor

University College London Hospitals

Collaborator

Source ClinicalTrials.gov

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