Literature DB >> 24664528

What is normal nasal airflow? A computational study of 22 healthy adults.

Kai Zhao1, Jianbo Jiang.   

Abstract

BACKGROUND: Nasal airflow is essential for the functioning of the human nose. Given individual variation in nasal anatomy, there is yet no consensus what constitutes normal nasal airflow patterns. We attempt to obtain such information that is essential to differentiate disease-related conditions.
METHODS: Computational fluid dynamics (CFD) simulated nasal airflow in 22 healthy subjects during resting breathing. Streamline patterns, airflow distributions, velocity profiles, pressure, wall stress, turbulence, and vortical flow characteristics under quasi-steady state were analyzed. Patency ratings, acoustically measured minimum cross-sectional area (MCA), and rhinomanometric nasal resistance (NR) were examined for potential correlations with morphological and airflow-related variables.
RESULTS: Common features across subjects included: >50% total pressure drop reached near the inferior turbinate head; wall shear stress, NR, turbulence energy, and vorticity were lower in the turbinate than in the nasal valve region. However, location of the major flow path and coronal velocity distributions varied greatly across individuals. Surprisingly, on average, more flow passed through the middle than the inferior meatus and correlated with better patency ratings (r = -0.65, p < 0.01). This middle flow percentage combined with peak postvestibule nasal heat loss and MCA accounted for >70% of the variance in subjective patency ratings and predicted patency categories with 86% success. Nasal index correlated with forming of the anterior dorsal vortex. Expected for resting breathing, the functional impact for local and total turbulence, vorticity, and helicity was limited. As validation, rhinomanometric NR significantly correlated with CFD simulations (r = 0.53, p < 0.01).
CONCLUSION: Significant variations of nasal airflow found among healthy subjects; Key features may have clinically relevant applications.
© 2014 ARS-AAOA, LLC.

Entities:  

Keywords:  Computational Fluid Dynamics (CFD); computer modeling for nasal airflow; nasal airflow dynamics

Mesh:

Year:  2014        PMID: 24664528      PMCID: PMC4144275          DOI: 10.1002/alr.21319

Source DB:  PubMed          Journal:  Int Forum Allergy Rhinol        ISSN: 2042-6976            Impact factor:   3.858


  24 in total

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  36 in total

1.  First Steps to Develop and Validate a CFPD Model in Order to Support the Design of Nose-to-Brain Delivered Biopharmaceuticals.

Authors:  Lucas Engelhardt; Martina Röhm; Chrystelle Mavoungou; Katharina Schindowski; Annette Schafmeister; Ulrich Simon
Journal:  Pharm Res       Date:  2016-02-17       Impact factor: 4.200

2.  Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency.

Authors:  Courtney Gaberino; John S Rhee; Guilherme J M Garcia
Journal:  Respir Physiol Neurobiol       Date:  2017-01-09       Impact factor: 1.931

3.  Normative ranges of nasal airflow variables in healthy adults.

Authors:  Azadeh A T Borojeni; Guilherme J M Garcia; Masoud Gh Moghaddam; Dennis O Frank-Ito; Julia S Kimbell; Purushottam W Laud; Lisa J Koenig; John S Rhee
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-07-02       Impact factor: 2.924

4.  Computational fluid dynamics after endoscopic endonasal skull base surgery-possible empty nose syndrome in the context of middle turbinate resection.

Authors:  Guillermo Maza; Chengyu Li; Jillian P Krebs; Bradley A Otto; Alexander A Farag; Ricardo L Carrau; Kai Zhao
Journal:  Int Forum Allergy Rhinol       Date:  2018-11-29       Impact factor: 3.858

5.  Computational fluid dynamics as surgical planning tool: a pilot study on middle turbinate resection.

Authors:  Kai Zhao; Prashant Malhotra; David Rosen; Pamela Dalton; Edmund A Pribitkin
Journal:  Anat Rec (Hoboken)       Date:  2014-11       Impact factor: 2.064

6.  Computational fluid dynamics and trigeminal sensory examinations of empty nose syndrome patients.

Authors:  Chengyu Li; Alexander A Farag; James Leach; Bhakthi Deshpande; Adam Jacobowitz; Kanghyun Kim; Bradley A Otto; Kai Zhao
Journal:  Laryngoscope       Date:  2017-03-09       Impact factor: 3.325

7.  Sinus irrigations before and after surgery-Visualization through computational fluid dynamics simulations.

Authors:  Kai Zhao; John R Craig; Noam A Cohen; Nithin D Adappa; Sammy Khalili; James N Palmer
Journal:  Laryngoscope       Date:  2015-10-15       Impact factor: 3.325

8.  Investigation of the abnormal nasal aerodynamics and trigeminal functions among empty nose syndrome patients.

Authors:  Chengyu Li; Alexander A Farag; Guillermo Maza; Sam McGhee; Michael A Ciccone; Bhakthi Deshpande; Edmund A Pribitkin; Bradley A Otto; Kai Zhao
Journal:  Int Forum Allergy Rhinol       Date:  2017-11-22       Impact factor: 3.858

9.  Correlation between Subjective Nasal Patency and Intranasal Airflow Distribution.

Authors:  Kevin P Casey; Azadeh A T Borojeni; Lisa J Koenig; John S Rhee; Guilherme J M Garcia
Journal:  Otolaryngol Head Neck Surg       Date:  2017-01-31       Impact factor: 3.497

10.  Modeling congenital nasal pyriform aperture stenosis using computational fluid dynamics.

Authors:  Tirth R Patel; Chengyu Li; Jillian Krebs; Kai Zhao; Prashant Malhotra
Journal:  Int J Pediatr Otorhinolaryngol       Date:  2018-04-05       Impact factor: 1.675

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