LAUSR

Obstructive sleep apnea (OSA) is characterized by recurrent upper airway collapse during sleep, resulting from interactions between aerodynamic forces, neuromuscular activation, and tissue properties. This study introduces a novel methodology for analyzing airway wall dynamics by incorporating acceleration-based metrics into computational fluid dynamics (CFD) simulations to better understand the pathophysiology of airway collapse in OSA. A patient with OSA underwent magnetic resonance imaging (MRI) to capture airway anatomy and motion under sleep-like sedation. A virtual airway model was segmented from high-resolution MRI and animated by registering dynamic cine MRI sequences. CFD simulations using this prescribed wall motion and computed 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 non-linear, indicating that internal forces vary dynamically throughout the respiratory cycle. This study demonstrates a novel approach for assessing airway wall dynamics in OSA using airway wall acceleration. By analyzing pressure-acceleration relationships, we distinguished passive collapse from active neuromuscular motion, enabling more precise phenotyping of OSA patients. Future research may extend this methodology to larger populations and explore its potential for personalized treatment planning.