Automated Apnoea Detection in Preterms on Non-invasive Ventilation

NCT07794007 | Not Yet Recruiting

• 1 other identifier: 371040
• Study Type: observational
• Target: 30
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Apnoea; Prematurity

Keywords

apnoea detection; apnoea classification; obstructive apnoea; central apnoea; preterm infant

Outcome Measures

Primary Outcomes (1)

• Number of apnoeic episodes correctly identified by the automated machine learning model

  From enrollment to the end of monitoring at eight hours

Secondary Outcomes (1)

• The proportion of apnoeas correctly classified as central or obstructive by the automated machine learning model

  From enrollment to the end of monitoring at eight hours

Interventions

Diaphragmatic electromyography DIAGNOSTIC_TEST

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 betwe

Also known as: Airway pressure and flow measurements, Recording of peripheral oxygen saturation levels

Eligibility Criteria

• Age: Up to 36 Weeks
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Infants admitted and cared for at the Neonatal Intensive Care Unit at King's College Hospital

You may qualify if:

• 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.

You may not qualify if:

• Infants born above 32 weeks of gestation.
• Infants with known major congenital abnormalities.
• Infants above 36 weeks postmenstrual age (PMA).
• Non-English speakers.

Sponsors & Collaborators

• King's College Hospital NHS Trust: lead

MeSH Terms

Conditions

Apnea; Premature Birth; Sleep Apnea, Central

Condition Hierarchy (Ancestors)

Respiration Disorders; Respiratory Tract Diseases; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Obstetric Labor, Premature; Obstetric Labor Complications; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Sleep Apnea Syndromes; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Nervous System Diseases

Study Officials

• Anne Greenough, Professor

  King's College Hospital NHS Trust

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ourania Kaltsogianni, MD (Res)

CONTACT

0044+02032999000; ourania.kaltsogianni@nhs.net

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 25, 2026
• First Posted: August 31, 2026
• Study Start: September 7, 2026
• Primary Completion (Estimated): July 27, 2027
• Study Completion (Estimated): July 27, 2027
• Last Updated: August 31, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share