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AI to predict how air pollution harms unborn babies

NCT ID NCT06340971

Knowledge-focused Sponsor: Queen Mary University of London Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now This study
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 25, 2026 · Last updated Jun 27, 2026 · Updated 1 time

Summary

This study will use artificial intelligence to analyze health records from 200,000 pregnant women in London, combined with air pollution data, to predict the risk of preterm birth. The goal is to understand when and how pollution exposure is most harmful during pregnancy. Researchers hope to create practical advice, such as safer travel routes, to help pregnant women reduce their exposure.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

What this could lead to
If successful, this could lead to better public health guidance to help pregnant women reduce their exposure to air pollution and lower the risk of preterm birth.
What could go wrong
This is an observational study using existing data, so it cannot prove cause and effect. The AI model may not work perfectly for all groups or locations outside London.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 200,000 people

The number the study aims to enrol. It can still change while the study runs.

Started

Nov 2024

Expected to finish

Nov 2029

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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.

Ages

18 years and older

Sex

Female participants only

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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.

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Conditions

The condition(s) this trial relates to.

Premature Birth

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Premature birth

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    2 sites. The list below names each one and where it is.

  2. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  3. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

Contacts and locations

Locations

  • Anna David

    RECRUITING

    London, NW1 2PG, United Kingdom

  • Tina Chowdhury

    RECRUITING

    London, E14NS, United Kingdom

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