Air Pollution and Pregnancy

Premature Birth Clinical Trial

— PTB  

Official title:

The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06340971
• Other study ID #: 331247
• Status: Recruiting
• Start date: September 1, 2024
• Est. completion date: August 31, 2029

Study information

• Verified date: March 2024
• Source: Queen Mary University of London
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical 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. 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.

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

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200000
• Est. completion date: August 31, 2029
• Est. primary completion date: August 31, 2029
• Gender: Female
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Premature Birth

Intervention

Other:
• Policy
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.

Locations

Country	Name	City	State
United Kingdom	Anna David	London
United Kingdom	Tina Chowdhury	London

Sponsors (2)

Lead Sponsor	Collaborator
Queen Mary University of London	University College London Hospitals

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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.	42 months