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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:
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Continuous positive airway pressure (CPAP) is an essential, non-invasive therapy for treating various respiratory conditions in the Neonatal Intensive Care Units (NICU). CPAP is an effective treatment for respiratory distress syndrome, apneas, or after extubation, exerting its physiological benefits by maintaining upper airway patency and functional residual capacity. Bubble CPAP (bCPAP) is the most widely used CPAP due to its low cost and ease of use. It consists of an inspiratory tube carrying heated and humidified air, a nasal interface, and an expiratory tube immersed in a water chamber. The generation of bubbles in the water chamber by exhaled gas creates low amplitude and high-frequency pressure oscillations that are transmitted back to the chest. Successful CPAP requires constant transmission of the pressure via an unobstructed circuit. However, this is difficult to achieve in practice due to inadequate interface, leaks from an open mouth, and obstructed airway. As a result, bCPAP requires frequent manual checks by nurses and respiratory therapists to ensure that the circuit is secure and unobstructed.
As a proposed solution, bCPAP sounds heard in the patient room or upon auscultation are routinely used to assess the effectiveness of CPAP therapy. This sound can be heard both from the water tank creating the vibrations and during auscultation with a stethoscope, as the sound vibration is transmitted to the neonatal lungs. In the current era of digital technology, acoustic sounds can be converted to electronic signals for further processing and analysis.
We hypothesize that continuous recording and analysis of bCPAP sounds could be used as a proxy for real-time objective monitoring of the pressure transmitted to infants' lungs.
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30 participants in 2 patient groups
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Wissam Shalish, MD, PhD
Data sourced from clinicaltrials.gov
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