[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"apnoea\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:apnoea":24},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":4,"maxAge":17,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":26,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":33,"lastUpdatePostDateStruct":34,"startDateStruct":37,"completionDateStruct":39,"leadSponsor":41,"locationsCount":4},"100653984","automated-apnoea-detection-in-preterms-on-non-invasive-ventilation-100653984",false,"NCT07794007","Automated Apnoea Detection in Preterms on Non-invasive Ventilation","Prospective Observational Study of Automated Apnoea Detection in Preterm Infants Receiving Non-invasive Respiratory Support","Inclusion Criteria:\n\nPreterm infants \\\u003C32 weeks of gestation at birth and up to 36 weeks postmenstrual age, on non-invasive respiratory support including:\n\n* non-invasive positive pressure ventilation (NIPPV)\n* nasal continuous positive airway pressure (CPAP)\n* heated humidified high flow nasal cannula (HHFNC) oxygen, either as primary or post extubation respiratory support.\n\nExclusion Criteria:\n\n* Infants born above 32 weeks of gestation.\n* Infants with known major congenital abnormalities.\n* Infants above 36 weeks postmenstrual age (PMA).\n* Non-English speakers.","ALL","36 Weeks",{"count":19,"type":20},30,"ESTIMATED","OBSERVATIONAL","The aim of this study is to monitor the frequency of apnoeas (pauses in breathing) on various methods of non-invasive respiratory support that are detected by an automated machine-learning (ML) model based on diaphragmatic electromyography (dEMG), in infants born at less than 32 weeks of gestation.\n\nOur hypothesis is that the ML algorithm will improve identification of apnoeic episodes and their classification to central or obstructive.\n\nThe study will measure outcomes including the number of apnoeic episodes during the monitoring period, their classification to central and obstructive apnoeas and the predictive ability of the machine-learning algorithm to correctly identify and classify these episodes compared to those documented in nursing charts. Correct classification of apnoeic episodes may help identify underlying causes that require specific intervention.",[24,25],"Apnoea","Prematurity",[27,28,29,30,31],"apnoea detection","apnoea classification","obstructive apnoea","central apnoea","preterm infant","NOT_YET_RECRUITING","2026-08-25",{"date":35,"type":36},"2026-08-31","ACTUAL",{"date":38,"type":20},"2026-09-07",{"date":40,"type":20},"2027-07-27",{"name":42,"class":43},"King's College Hospital NHS Trust","OTHER"]