[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100542292":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":7,"centralContacts":7,"locations":12,"responsibleParty":52,"collaborators":54,"id":57,"slug":58,"hasResults":59,"nctId":60,"briefTitle":61,"officialTitle":62,"acronym":63,"eligibilityCriteria":64,"healthyVolunteers":59,"sex":65,"minAge":66,"maxAge":7,"enrollmentInfo":67,"targetDuration":70,"studyType":71,"phases":7,"briefSummary":72,"conditions":73,"keywords":75,"overallStatus":15,"whyStopped":7,"lastUpdateSubmitDate":81,"lastUpdatePostDateStruct":82,"startDateStruct":85,"completionDateStruct":87,"leadSponsor":89,"locationsCount":90},{"fullName":5,"class":6},"Queen Mary University of London","OTHER",null,[9],{"type":6,"name":10,"description":11,"armGroupLabels":7,"otherNames":7},"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.\n\nWe 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.",[13,36],{"facility":14,"status":15,"city":16,"state":7,"zip":17,"country":18,"countryCode":19,"cosmosGeoPoint":20,"geoPoint":25,"contacts":26},"Tina Chowdhury","RECRUITING","London","E14NS","United Kingdom","UK",{"type":21,"coordinates":22},"Point",[23,24],-0.12574,51.50853,{"lat":24,"lon":23},[27,32],{"name":14,"role":28,"phone":29,"phoneExt":30,"email":31},"CONTACT","0207882","7560","t.t.chowdhury@qmul.ac.uk",{"name":33,"role":28,"phone":29,"phoneExt":34,"email":35},"Rhona Atkins","7272","rhona.atkin@nhs.net",{"facility":37,"status":15,"city":16,"state":7,"zip":38,"country":18,"countryCode":19,"cosmosGeoPoint":39,"geoPoint":41,"contacts":42},"Anna David","NW1 2PG",{"type":21,"coordinates":40},[23,24],{"lat":24,"lon":23},[43,48],{"name":44,"role":28,"phone":45,"phoneExt":46,"email":47},"Yaa Acheampong","020 3447","6164","y.acheampong@nhs.net",{"name":37,"role":28,"phone":49,"phoneExt":50,"email":51},"0203 447","9400","a.david@nhs.net",{"type":53,"investigatorFullName":7,"investigatorTitle":7,"investigatorAffiliation":7,"oldNameTitle":7,"oldOrganization":7},"SPONSOR",[55],{"name":56,"class":6},"University College London Hospitals","100542292","air-pollution-and-pregnancy-100542292",false,"NCT06340971","Air Pollution and Pregnancy","The Effects of Air Pollution on Pregnancy and Adverse Birth Outcomes","PTB","Inclusion Criteria:\n\n* 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.\n\nExclusion Criteria:\n\n* 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.","FEMALE","18 Years",{"count":68,"type":69},200000,"ESTIMATED","1 Year","OBSERVATIONAL","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.\n\nWe 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.",[74],"Premature Birth",[76,77,78,79,80],"Preterm birth","Pregnancy","Premature birth","Air pollution","Air quality","2025-11-21",{"date":83,"type":84},"2025-11-24","ACTUAL",{"date":86,"type":84},"2024-11-01",{"date":88,"type":69},"2029-11-30",{"name":5,"class":6},2]