Quantifying Neuromuscular and Pressure Force Dynamics in Obstructive Sleep Apnea: A Novel Computational Fluid Dynamics Approach Using Airway Wall Acceleration

利用气道壁加速度量化阻塞性睡眠呼吸暂停中的神经肌肉和压力动力学:一种新型的计算流体动力学方法

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Abstract

Obstructive sleep apnea (OSA) is characterized by recurrent upper airway collapse during sleep, resulting from interactions between aerodynamic forces, and neuromuscular activation of structures surrounding the airway. This study introduces a novel methodology for inferring the neuromuscular activity noninvasively from airway wall acceleration. The results will allow identification of the triggers for movements such as genioglossus advancement and to assess why they fail in patients with OSA. A patient with OSA underwent magnetic resonance imaging (MRI) to capture airway anatomy and motion under sleeplike sedation. A virtual airway model was segmented from high-resolution MRI and animated by registering dynamic cine MRI sequences. Computational fluid dynamics (CFD) simulations with this prescribed wall motion were used to compute airflow pressure forces acting on the airway wall. By quantifying airway wall acceleration and comparing it to airflow pressure forces, we inferred the contribution of internal forces, consisting of neuromuscular activation and tissue elasticity. Pressure-acceleration analysis at the soft palate, tongue, and epiglottis revealed distinct force imbalances leading to airway collapse and dilation. During inhalation, airway collapse started before peak negative pressure, suggesting insufficient neuromuscular activation. During exhalation, substantial neuromuscular-driven motion occurred. The relationship between airway pressure and acceleration was nonlinear, indicating that internal forces vary dynamically throughout the respiratory cycle. This study demonstrates a novel approach for assessing neuromuscular activation in OSA using airway wall acceleration. By analyzing pressure-acceleration relationships, passive collapse was distinguished from active neuromuscular motion, enabling more precise phenotyping of OSA patients.

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