CO2 monitoring in non-invasive ventilation (NIV) helmets: A bench study assessment of sensor integration
Noninvasive ventilation (NIV) is a well-established technique for managing acute respiratory failure in various clinical settings. However, safety concerns in clinical NIV applications emerge due to the absence of robust monitoring and alarm systems, potentially leading to issues such as CO2 rebreat...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025008801 |
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| Summary: | Noninvasive ventilation (NIV) is a well-established technique for managing acute respiratory failure in various clinical settings. However, safety concerns in clinical NIV applications emerge due to the absence of robust monitoring and alarm systems, potentially leading to issues such as CO2 rebreathing during flow-block events. This work aims to enhance the safety and monitoring of NIV systems by studying the integration of two types of carbon dioxide (CO2) sensors within NIV helmets. The investigation encompasses two main analyses. The first analysis explores the impact of varying the fresh inlet gas flow rate on local CO2 concentrations within the helmet. The second analysis investigates the response of CO2 sensors during simulated flow-block events, a critical safety concern in NIV therapy. In both analyses the effect of the sensor positioning is also investigated. Results demonstrate that higher fresh gas flow rates enhance CO2 washout within the helmet, highlighting the importance of optimizing gas flow rates to mitigate CO2 rebreathing. The positioning of CO2 sensors within the helmet was also found to significantly influence measurements by affecting signal stability and response to flow-block events. Overall, this study demonstrated the potential of integrating CO2 sensors within NIV helmets to enhance patient safety and treatment effectiveness. The knowledge gained from this study can be used to guide the design and optimization of NIV systems. |
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| ISSN: | 2405-8440 |