Acoustics as a Metric of Airway Pressure in Premature Infants Using Bubble Continuous Positive Airway Pressure

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age28-32
SponsorMcGill University Health Centre/Research Institute of the McGill University Health Centre

About this trial

This is an observational, proof-of-concept, feasibility study where 30 preterm infants on bubble CPAP with gestational age \< 32+0 weeks will be recruited from the neonatal intensive care unit (NICU) at the Montreal Children's Hospital.

The study's main goals are:

1. To determine the relationship between ambient bubbling sounds and delivered pressures in preterm infants on bCPAP. 2. To determine the relationship between transmitted bubbling sounds and airway pressures transmitted to the lungs of preterm infants on bCPAP. 3. To develop models to predict delivered and transmitted bCPAP pressures from the acoustic properties of bubbling sounds.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Infants on bCPAP with gestational age < 32+0 weeks

Postmenstrual age between 28+0 and 36+6 weeks at the time of the study

Postnatal age greater than 168 hours (7 days) at the time of the study

On the bubble CPAP device with binasal prongs at the time of the study

Disqualifiers

Infants with known major congenital anomalies

Infants with known congenital heart disorders

Infants with known neuromuscular disease

Infants receiving ventilator-derived CPAP at the time of the study

Trial population

Preterm infants admitted to the neonatal intensive care unit at the Montreal Children's Hospital.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • External pressure transducer

    Device

    The delivered CPAP pressure will be measured using an ultra-thin, multi-use catheter pressure transducer inserted into a port in the expiratory limb of the bubble CPAP circuit.

  • Standard microphone

    Device

    The bubble sound of the water tank will be collected with a standard condenser microphone directly affixed to the pole holding the water tank, with a secure clip.

  • Wireless acoustic sensor

    Device

    The wireless acoustic sensor contains a dual microphone capable of capturing target sounds as well as ambient noise. The frequencies associated with ambient noise will be subtracted to maximize the signal-to-noise ratio of the bubble sound waveform. The wireless sensor will be placed on the suprasternal notch of the infant for monitoring the bubble sounds transmitted to the lungs and secured using a silicone-based tape approved for use in neonates. Data will be transmitted in real-time to a research-dedicated tablet using the Bluetooth Communication Controller and stored for future analysis.

  • Internal pressure transducer

    Device

    The transmitted CPAP pressure will be measured using an ultra-thin, single-use catheter pressure transducer inserted through the mouth to the level of the infant's nasopharynx. The data will be acquired with a sampling rate of 10kHz and stored for later analysis.

Treatment groups

30 Participants
are divided into 2 treatment groups
Group A: Phase I: 15 preterm infants on Bubble Continuous Positive Airway Pressure (bCPAP)2 interventions
Group B: Phase II: 15 preterm infants on Bubble Continuous Positive Airway Pressure (bCPAP)4 interventions

Trial outcomes

Primary outcomes

1

Pressure

The mean pressure and the standard deviation of the pressure will be computed for each segment.

Time frame
3 hours
2

External bubble CPAP sounds

Two metrics will be computed: 1. The root mean square (RMS) 2. The power contained between a pre-determined range within each segment; We will determine the range that contains 80% of the signal power, in order to minimize other noises from bubble CPAP signal.

Time frame
3 hours
3

Internal bubble CPAP sounds

1. We will apply biomedical signal processing methods to separate the bubbling sounds from breathing sounds. Then, RMS and power will be computed. 2. We will use the Pearson correlation coefficient to compute the relationship between bubbling sound and pressure metrics. We will evaluate the linear regression models to identify the combination of sound metrics and covariates with the highest predictive accuracy.

Time frame
3 hours

Secondary outcomes

Other outcomes

Sponsors and contacts

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