Characterizing airflow profile in the postoperative maxillary sinus by using computational fluid dynamics modeling: A pilot study.

Journal Article (Journal Article)

BACKGROUND: Maxillary antrostomy is commonly performed during endoscopic sinus surgery. Little is known about the association surrounding recalcitrant maxillary sinusitis, antrostomy size, and intranasal airflow changes. Furthermore, the interaction between sinus mucosa and airflow is poorly understood. This study used computational fluid dynamics (CFD) modeling to investigate postoperative airflow characteristics between diseased and nondiseased maxillary sinuses in subjects with recurrent disease. METHODS: A retrospective review of patients from a tertiary-level academic rhinology practice was performed. Seven subjects with endoscopic evidence of postoperative maxillary sinus disease that presented as chronic unilateral crusting at least 1 year after bilateral maxillary antrostomies were selected. A three-dimensional model of each subject's sinonasal cavity was created from postoperative computed tomographies and used for CFD analysis. RESULTS: Although the variables investigated between diseased and nondiseased sides were not statistically significant, the diseased side in six subjects had a smaller antrostomy, and five of these subjects had both reduced nasal unilateral airflow and increased unilateral nasal resistance on the diseased side. The ratio of posterior wall shear stress (WSS) of the maxillary sinus to the total WSS was higher on the diseased side in six subjects. Results also showed strong correlations between antrostomy and CFD variables on the diseased side than on the nondiseased side. CONCLUSION: This pilot study showed that the majority of the simulated sinonasal models exhibited common characteristics on the side with persistent disease, such as smaller antrostomy, reduced nasal airflow, increased nasal resistance, and increased posterior WSS. Although statistical significance was not established, this study provided preliminary insight into variables to consider in a larger cohort study.

Full Text

Duke Authors

Cited Authors

  • Choi, KJ; Jang, DW; Ellison, MD; Frank-Ito, DO

Published Date

  • January 2016

Published In

Volume / Issue

  • 30 / 1

Start / End Page

  • 29 - 36

PubMed ID

  • 26867527

Electronic International Standard Serial Number (EISSN)

  • 1945-8932

Digital Object Identifier (DOI)

  • 10.2500/ajra.2016.30.4266


  • eng

Conference Location

  • United States