ArticleScientific reports2024
The effect of including dynamic imaging derived airway wall motion in CFD simulations of respiratory airflow in patients with OSA.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Patient-Specific Computational Fluid Dynamics Modeling of Airway Collapse in Infants With Robin Sequence.International journal for numerical methods in biomedical engineering · 2026Article
- The Pharyngeal Resistance Index-A Promising Diagnostic Tool in OSA.Journal of sleep research · 2026Article
- Biomechanical analysis of upper airway airflow characteristics in children.Sleep & breathing = Schlaf & Atmung · 2026Article
- Impact of Surgical Diaphragmatic Repair on Central Airway Shape in Neonatal Congenital Diaphragmatic Hernia.Pediatric pulmonology · 2026Article
- Quantifying Neuromuscular and Pressure Force Dynamics in Obstructive Sleep Apnea: A Novel Computational Fluid Dynamics Approach Using Airway Wall Acceleration.Journal of biomechanical engineering · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Obstructive sleep apnea (OSA) is an airway disease caused by periodic collapse of the airway during sleep. Imaging-based subject-specific computational fluid dynamics (CFD) simulations allow non-invasive assessment of clinically relevant metrics such as total pressure loss (TPL) in patients with OSA. However, most of such studies use static airway geometries, which neglect physiological airway motion. This study aims to quantify how much the airway moves during the respiratory cycle, and to determine how much this motion affects CFD pressure loss predictions. Motion of the airway wall was quantified using cine MRI data captured over a single respiratory cycle in three subjects with OSA. Synchronously-measured respiratory airflow was used as the flow boundary condition for all simulations. Simulations were performed for full respiratory cycles with 5 different wall boundary conditions: (1) a moving airway wall, and static airway walls at (2) peak inhalation, (3) end inhalation, (4) peak exhalation, and (5) end exhalation. Geometric analysis exposed significant local airway cross-sectional area (CSA) variability, with local CSA varying as much as 300%. The comparative CFD simulations revealed the discrepancies between dynamic and static wall simulations are subject-specific, with TPL differing by up to 400% between static and dynamic simulations. There is no consistent pattern to which static wall CFD simulations overestimate or underestimate the airway TPL. This variability underscores the complexity of accurately modeling airway physiology and the importance of considering dynamic anatomical factors to predict realistic respiratory airflow dynamics in patients with OSA.
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