Patient-Specific Computational Fluid Dynamics Modeling of Airway Collapse in Infants With Robin Sequence

Robin Sequence (RS) is a congenital condition in which patients experience dynamic, periodic obstruction or collapse of the upper airway due to an underdeveloped jaw and a posteriorly displaced tongue. Current clinical techniques for evaluating airway obstruction do not provide quantifiable data and fail to account for the dynamic nature of obstruction or collapse. There is no standardized criterion to characterize obstruction or collapse severity. This study presents the first method that extracts airway motion from 4-dimensional computed tomography and performs a patient-specific, moving-mesh computational fluid dynamics (CFD) analysis of RS patients with complete airway collapse or obstruction. To quantify the effects of airway collapse, airflow dynamics are analyzed using both instantaneous metrics (velocity, pressure, and energy dissipation rate) and cycle-averaged metrics (resistive work of breathing). These results are compared between a collapsing airway and its synthetic non-collapsing counterpart. To validate the synthetic non-collapsing case, it is further compared with a patient-specific non-collapsing airway. The results show that, to achieve the same tidal volume, the collapsing case requires significantly greater computed breathing effort (7.55 mJ/cycle) than the synthetic non-collapsing case (3.68 mJ/cycle). The shorter inspiration time due to the collapse leads to a higher inlet velocity, resulting in 1.7 times the maximum velocity during peak inspiration (just prior to collapse) and a 2.7-fold higher pressure drop in the collapsing case compared to the synthetic non-collapsing case. At the onset of collapse, a sharp spike in localized energy dissipation rate is observed due to the abrupt deceleration and dissipation of peak flow velocities. This methodology provides a novel approach to understanding the airflow dynamics of RS patients with airway collapse. It enables quantitative comparison between collapsing and non-collapsing airways, thereby offering the potential to support more informed and objective clinical decision-making.