4.5 Article

Analysis of Upper Airway Flow Dynamics in Robin Sequence Infants Using 4-D Computed Tomography and Computational Fluid Dynamics

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ANNALS OF BIOMEDICAL ENGINEERING
卷 51, 期 2, 页码 363-376

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SPRINGER
DOI: 10.1007/s10439-022-03036-6

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Respiratory flow; Upper airway obstruction; CFD; 4D-CT

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This study utilized 4D computed tomography and computational fluid dynamics to assess the severity of upper airway obstruction (UAO) in patients with Robin Sequence. The results showed significant variations in UAO severity and correlation with clinical respiratory support. These findings may contribute to the improvement of understanding and management of UAO in Robin Sequence patients.
Robin Sequence (RS) is a potentially fatal craniofacial condition characterized by undersized jaw, posteriorly displaced tongue, and resultant upper airway obstruction (UAO). Accurate assessment of UAO severity is crucial for management and diagnosis of RS, yet current evaluation modalities have significant limitations and no quantitative measures of airway resistance exist. In this study, we combine 4-dimensional computed tomography and computational fluid dynamics (CFD) to assess, for the first time, UAO severity using fluid dynamic metrics in RS patients. Dramatic intrapopulation differences are found, with the ratio between most and least severe patients in breathing resistance, energy loss, and peak velocity equal to 40:1, 20:1, and 6:1, respectively. Analysis of local airflow dynamics characterized patients as presenting with primary obstructions either at the location of the tongue base, or at the larynx, with tongue base obstructions resulting in a more energetic stenotic jet and greater breathing resistance. Finally, CFD-derived flow metrics are found to correlate with the level of clinical respiratory support. Our results highlight the large intrapopulation variability, both in quantitative metrics of UAO severity (resistance, energy loss, velocity) and in the location and intensity of stenotic jets for RS patients. These results suggest that computed airflow metrics may significantly improve our understanding of UAO and its management in RS.

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