Air Pollution and Pregnancy - Trial NCT06340971
Access comprehensive clinical trial information for NCT06340971 through Pure Global AI's free database. This phase not specified trial is sponsored by Queen Mary University of London and is currently Recruiting. The study focuses on Premature Birth. Target enrollment is 200000 participants.
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Study Focus
Sponsor & Location
Queen Mary University of London
Timeline & Enrollment
N/A
Sep 01, 2024
Aug 31, 2029
Primary Outcome
Machine learning model to predict the risk of preterm birth and adverse birth outcomes
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.
ICD-10 Classifications
Data Source
ClinicalTrials.gov
NCT06340971
Non-Device Trial