Automated Apnoea Detection in Preterms on Non-invasive Ventilation
Prospective Observational Study of Automated Apnoea Detection in Preterm Infants Receiving Non-invasive Respiratory Support
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.
Studie Overzicht
Toestand
Conditie
Interventie / Behandeling
Gedetailleerde beschrijving
When the clinical team identifies an infant is eligible for enrolment to the study and following verbal assent of the attending neonatal consultant, a member of clinical staff will initially approach the parents/ legal guardians of eligible infants and if they agree, a researcher. The parents will be provided with an information sheet about the study. The researchers will answer questions and respond to any concerns in a face-to-face meeting. Written informed consent will be obtained.
Electrical 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 between the exit of the SLE6000/ SLE6000N ventilators (Inspiration Healthcare, Croydon, UK) and the tubing that is attached to the patient. This will ensure pressure and flow data are recorded simultaneously with the sEMG signal. These data will also be fed to the SERA measuring device.
An SpO2 cable (SLE uSpO2, Inspiration Healthcare, Croydon, UK) will be connected to the patient and the ventilator for continuous (second by second) recording of SpO2 levels. These data will be time synced using post processing.
Participants will also be connected to the standard bedside monitor (Phillips Intellivue MX750) for the whole duration of the study.
Surface EMG, pressure, flow and SpO2 data will be synchronised. Two researchers will identify all apnoeic episodes and classify them as central, obstructive or noise.
The ML algorithm will also be used to identify and classify apnoeic episodes that occurred during the study duration.
Comparisons will be made between the apnoeic episodes identified the researchers, the ML algorithm and the nursing staff electronic patient records. These records include classification of an episode as apnoea or desaturation or bradycardia, its duration and any actions taken.
Studietype
Inschrijving (Geschat)
Contacten en locaties
Studiecontact
- Naam: Ourania Kaltsogianni, MD (Res)
- Telefoonnummer: 38494 0044+02032999000
- E-mail: ourania.kaltsogianni@nhs.net
Deelname Criteria
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
- Kind
Accepteert gezonde vrijwilligers
Bemonsteringsmethode
Studie Bevolking
Beschrijving
Inclusion Criteria:
Preterm infants <32 weeks of gestation at birth and up to 36 weeks postmenstrual age, on non-invasive respiratory support including:
- 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.
Exclusion Criteria:
- Infants born above 32 weeks of gestation.
- Infants with known major congenital abnormalities.
- Infants above 36 weeks postmenstrual age (PMA).
- Non-English speakers.
Studie plan
Hoe is de studie opgezet?
Ontwerpdetails
Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Tijdsspanne |
|---|---|
|
Number of apnoeic episodes correctly identified by the automated machine learning model
Tijdsspanne: From enrollment to the end of monitoring at eight hours
|
From enrollment to the end of monitoring at eight hours
|
Secundaire uitkomstmaten
Uitkomstmaat |
Tijdsspanne |
|---|---|
|
The proportion of apnoeas correctly classified as central or obstructive by the automated machine learning model
Tijdsspanne: From enrollment to the end of monitoring at eight hours
|
From enrollment to the end of monitoring at eight hours
|
Medewerkers en onderzoekers
Onderzoekers
- Hoofdonderzoeker: Anne Greenough, Professor, King's College Hospital NHS Trust
Studie record data
Bestudeer belangrijke data
Studie start (Geschat)
Primaire voltooiing (Geschat)
Studie voltooiing (Geschat)
Studieregistratiedata
Eerst ingediend
Eerst ingediend dat voldeed aan de QC-criteria
Eerst geplaatst (Werkelijk)
Updates van studierecords
Laatste update geplaatst (Werkelijk)
Laatste update ingediend die voldeed aan QC-criteria
Laatst geverifieerd
Meer informatie
Termen gerelateerd aan deze studie
Trefwoorden
Aanvullende relevante MeSH-voorwaarden
- Urogenitale ziekten
- Ziekten van het zenuwstelsel
- Vrouwelijke urogenitale ziekten en zwangerschapscomplicaties
- Verloskundige arbeid, voortijdig
- Verloskundige arbeidscomplicaties
- Zwangerschap Complicaties
- Ziekten van de luchtwegen
- Ademhalingsstoornissen
- Slaap-waakstoornissen
- Tekenen en symptomen, ademhaling
- Slaapstoornissen, intrinsiek
- Dyssomnieën
- Slaapapneusyndromen
- Pathologische aandoeningen, tekenen en symptomen
- Tekenen en symptomen
- Voortijdige geboorte
- Apneu
- Slaapapneu, Centraal
Andere studie-ID-nummers
- 371040
Plan Individuele Deelnemersgegevens (IPD)
Bent u van plan om gegevens van individuele deelnemers (IPD) te delen?
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
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