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PMID: 34585755 Published · ppublish English

Linking Chronic Otitis Media and Nasal Obstruction: A CFD Approach.

The Laryngoscope ·Vol. 132 ·No. 6 ·2022-00-00

Burgos MA, Pardo A, Rodríguez R, Rodríguez-Balbuena B, Castro D, Piqueras F, Esteban F

Abstract

To investigate a possible relationship between altered nasal flow and chronic otitis media (COM) using computational fluid dynamics (CFD). Retrospective case series. Retrospective cohort sample of CT scans from patients with COM and controls without COM to compare the results of various nasal airflow parameters determined by CFD between a group of patients with COM (N = 60) and a control group of subjects without any evidence of ear disease (N = 81). The CT were subjected to various procedures to carry out CFD studies, determining the resistance to nasal flow, the proportion of flow through the right and left nasal cavity, and two nondimensional estimators. The results of CFD studies between patients with COM and controls were compared. Whereas only 12.3% of the controls had CFD alteration (10 out of 81), 43.3% of the patients suffering COM displayed alterations of our nondimensional parameters R-ϕ (26 out of 60). According to our results, the incidence of alterations in nasal airflow by studying with CFD is significantly higher in patients with COM than in controls. To our knowledge, this is the first article linking nasal cavity and COM using a CFD approach. Our results support the hypothesis that nasal flow alterations could be implicated in the etiopathogenesis of the COM. 4 Laryngoscope, 132:1224-1230, 2022.

Keywords
CFD tools Chronic otitis media computational fluid dynamics deviated nasal septum dimensionless estimators middle ear cholesteatoma nasal obstruction
MeSH 主题词
Chronic Disease Computer Simulation Humans Hydrodynamics Nasal Cavity Nasal Obstruction/diagnostic imaging,etiology Otitis Media/complications Retrospective Studies
Article Info
Journal
The Laryngoscope
Abbr.
Laryngoscope
ISSN
1531-4995
Published
2022-00-00
Language
English
Country/Region
United States
NLM ID
8607378
Analysis Services
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