The Application of Computational Fluid Dynamics in the Evaluation of Laryngotracheal Pathology. (May 2019)

• Record Type: Journal Article
• Authors: Mason, Eric C.; McGhee, Samuel; Zhao, Kai; Chiang, Tendy; Matrka, Laura
• Is Part Of: Annals of otology, rhinology & laryngology. Volume 128:Number 5(2019)
• Page Start: 453
• Page End: 459
• Publication Date: 2019-05
• Subjects: CFD -- computational fluid dynamics -- laryngotracheal stenosis -- subglottic stenosis -- tracheal stenosis -- virtual modeling; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0003489419826601
• Languages: English
• ISSNs: 0003-4894

Abstract

Objectives: Laryngotracheal stenosis and obstruction can be challenging to manage. Traditional assessment tools are limited in clinical correlation. Three-dimensional computational fluid dynamics (CFD) modeling is a novel technique used to analyze airflow dynamics. The objective of this study was to apply CFD to the human upper airway to explore its utility. Methods: CFD models were constructed on an adult patient with an obstructive tracheal lesion before and after intervention and on an adult with normal airway anatomy, using computed tomographic imaging obtained retrospectively. Key airflow metrics were calculated. Results: CFD provided detailed airway geometry. The normal airway had a peak flow velocity of 3.12 m/s, wall shear stress of 0.30 Pa, and resistance of 0.02 Pa/mL/s. The pathologic patient showed an elevated peak flow velocity of 12.25 m/s, wall shear stress of 3.90 Pa, and resistance of 0.22 Pa/mL/s. This was reflected clinically with dyspnea, stridor, and obstructive impairment via pulmonary function testing. Following treatment, peak flow velocity corrected to 3.95 m/s, wall shear stress to 0.72Pa, and resistance to 0.01 Pa/mL/s. Cross-sectional area improved to 190 mm 2 from a minimum of 53 mm 2 at the same segment. Stridor and dyspnea resolved. Conclusions: CFD metrics were calculated on the normal, diseased, and posttreatment upper airway. Variations were reflected in clinical symptoms. These methods could model surgical outcomes and anticipate disease severity.