Azadeh A T Borojeni1,2, Guilherme J M Garcia3,4, Masoud Gh Moghaddam1,2, Dennis O Frank-Ito5,6,7, Julia S Kimbell8, Purushottam W Laud9, Lisa J Koenig10, John S Rhee1. 1. Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. 2. Department of Biomedical Engineering, Marquette University & The Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. 3. Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. ggarcia@mcw.edu. 4. Department of Biomedical Engineering, Marquette University & The Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. ggarcia@mcw.edu. 5. Division of Head and Neck Surgery and Communication Sciences, Duke University Medical Center, Durham, NC, USA. 6. Computational Biology and Bioinformatics Program, Duke University, Durham, NC, USA. 7. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA. 8. Department of Otolaryngology/Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, NC, USA. 9. Division of Biostatistics, Institute for Health and Society, Medical College of Wisconsin, Milwaukee, WI, USA. 10. Department of Oral Medicine and Oral Radiology, Marquette University School of Dentistry, Milwaukee, WI, USA.
Abstract
PURPOSE: Virtual surgery planning based on computational fluid dynamics (CFD) simulations of nasal airflow has the potential to improve surgical outcomes for patients with nasal airway obstruction (NAO). Virtual surgery planning requires normative ranges of airflow variables, but few studies to date have quantified inter-individual variability of nasal airflow among healthy subjects. This study reports CFD simulations of nasal airflow in 47 healthy adults. METHODS: Anatomically accurate three-dimensional nasal models were reconstructed from cone beam computed tomography scans and used for steady-state inspiratory airflow simulations with a bilateral flowrate of 250 ml/s. Normal subjective sensation of nasal patency was confirmed using the nasal obstruction symptom evaluation and visual analog scale. Healthy ranges for several CFD variables known to correlate with subjective nasal patency were computed, including unilateral airflow, nasal resistance, airspace minimal cross-sectional area (mCSA), heat flux (HF), and surface area stimulated by mucosal cooling (defined as the area where HF > 50 W/m2). The normative ranges were targeted to contain 95% of the healthy population and computed using a nonparametric method based on order statistics. RESULTS: A wide range of inter-individual variability in nasal airflow was observed among healthy subjects. Unilateral airflow varied from 60 to 191 ml/s, airflow partitioning ranged from 23.8 to 76.2%, and unilateral mCSA varied from 0.24 to 1.21 cm2. These ranges are in good agreement with rhinomanometry and acoustic rhinometry data from the literature. A key innovation of this study are the normative ranges of flow variables associated with mucosal cooling, which recent research suggests is the primary physiological mechanism of nasal airflow sensation. Unilateral HF ranged from 94 to 281 W/m2, while the surface area stimulated by cooling ranged from 27.4 to 64.3 cm2. CONCLUSIONS: These normative ranges may serve as targets in future virtual surgery planning for patients with NAO.
PURPOSE: Virtual surgery planning based on computational fluid dynamics (CFD) simulations of nasal airflow has the potential to improve surgical outcomes for patients with nasal airway obstruction (NAO). Virtual surgery planning requires normative ranges of airflow variables, but few studies to date have quantified inter-individual variability of nasal airflow among healthy subjects. This study reports CFD simulations of nasal airflow in 47 healthy adults. METHODS: Anatomically accurate three-dimensional nasal models were reconstructed from cone beam computed tomography scans and used for steady-state inspiratory airflow simulations with a bilateral flowrate of 250 ml/s. Normal subjective sensation of nasal patency was confirmed using the nasal obstruction symptom evaluation and visual analog scale. Healthy ranges for several CFD variables known to correlate with subjective nasal patency were computed, including unilateral airflow, nasal resistance, airspace minimal cross-sectional area (mCSA), heat flux (HF), and surface area stimulated by mucosal cooling (defined as the area where HF > 50 W/m2). The normative ranges were targeted to contain 95% of the healthy population and computed using a nonparametric method based on order statistics. RESULTS: A wide range of inter-individual variability in nasal airflow was observed among healthy subjects. Unilateral airflow varied from 60 to 191 ml/s, airflow partitioning ranged from 23.8 to 76.2%, and unilateral mCSA varied from 0.24 to 1.21 cm2. These ranges are in good agreement with rhinomanometry and acoustic rhinometry data from the literature. A key innovation of this study are the normative ranges of flow variables associated with mucosal cooling, which recent research suggests is the primary physiological mechanism of nasal airflow sensation. Unilateral HF ranged from 94 to 281 W/m2, while the surface area stimulated by cooling ranged from 27.4 to 64.3 cm2. CONCLUSIONS: These normative ranges may serve as targets in future virtual surgery planning for patients with NAO.
Entities:
Keywords:
Computational fluid dynamics (CFD) simulations of nasal airflow; Mucosal cooling in nasal cavity; Nasal airway obstruction (NAO); Normative range in healthy subjects; Virtual surgery planning
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