Automated Apnoea Detection in Preterms on Non-invasive Ventilation
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeUp to 36
SponsorKing's College Hospital NHS Trust
About this trial
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.
Our hypothesis is that the ML algorithm will improve identification of apnoeic episodes and their classification to central or obstructive.
The 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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
non-invasive positive pressure ventilation (NIPPV)
nasal continuous positive airway pressure (CPAP)
heated humidified high flow nasal cannula (HHFNC) oxygen, either as primary or post extubation respiratory support.
Disqualifiers
Infants born above 32 weeks of gestation.
Infants with known major congenital abnormalities.
Infants above 36 weeks postmenstrual age (PMA).
Non-English speakers.
Trial population
Infants admitted and cared for at the Neonatal Intensive Care Unit at King's College Hospital
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Diaphragmatic electromyography
Diagnostic testElectrical activity of the diaphragm, airway pressure, flow and peripheral oxygen saturation levels will be recorded for a duration of eight hours. Transcutaneous diaphragm EMG (sEMG) will be monitored using three surface electrodes (3M Red Dot Foam monitoring electrode 2228, 3M, United Kingdom) that are placed on the infant's abdomen and sternum. The electrodes are connected to a small battery-operated measuring device (SERA, DEMCON; Makawi Medical Systems, the Netherlands) that amplifies and pre-processes the signals received from the electrodes. The pre-processed signals are sent via a Bluetooth connection to a receiving unit that performs higher level processing to derive the EMG signal and other measurements. These results are communicated via a wired connection to a bedside computer running SERA Graphical User Interface (GUI) software. Airway pressure and flow signals will be measured by a flow sensor and pressure tube (Sensirion AG, Stäfa, Switzerland) that will be placed betwe
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Number of apnoeic episodes correctly identified by the automated machine learning model
Time frame
From enrollment to the end of monitoring at eight hoursSecondary outcomes
1
The proportion of apnoeas correctly classified as central or obstructive by the automated machine learning model
Time frame
From enrollment to the end of monitoring at eight hoursLocations
Sponsors and contacts
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Source ClinicalTrials.gov
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