Predicting postsurgery nasal physiology with computational modeling: current challenges and limitations.

Published

Journal Article

High failure rates for surgical treatment of nasal airway obstruction (NAO) indicate that better diagnostic tools are needed to improve surgical planning. This study evaluates whether computer models based on a surgeon's edits of presurgery scans can accurately predict results from computer models based on postoperative scans of the same patient using computational fluid dynamics.Prospective study.Academic medical center.Three-dimensional nasal models were reconstructed from computed tomographic scans of 10 patients with NAO presurgery and 5 to 8 months postsurgery. To create transcribed-surgery models, the surgeon digitally modified the preoperative reconstruction in each patient to represent physical changes expected from surgery and healing. Steady-state, laminar, inspiratory airflow was simulated in each model under physiologic, pressure-driven conditions.Transcribed-surgery and postsurgery model variables were statistically different from presurgery variables at α = 0.05. Unilateral nasal resistance and airflow were not statistically different between transcribed-surgery and postsurgery models, but bilateral resistance was significantly different. Cross-sectional average pressures in transcribed surgery trended with postsurgery. Transcribed-surgery prediction errors of postsurgery bilateral resistance were within 10% to 20% and 20% to 30% in 5 and 4 subjects, respectively. Prediction errors for unilateral resistance were <10%, 10% to 20%, and 20% to 30% in 1, 2, and 4 subjects, respectively.Computational models with modifications mimicking actual surgery and healing have the potential to predict postoperative outcomes. However, software to effectively translate virtual surgery steps into computational models is lacking. The ability to account for healing factors and the current limited virtual surgery tools are challenges that need to be overcome for greater accuracy.

Full Text

Duke Authors

Cited Authors

  • Frank-Ito, DO; Kimbell, JS; Laud, P; Garcia, GJM; Rhee, JS

Published Date

  • November 2014

Published In

Volume / Issue

  • 151 / 5

Start / End Page

  • 751 - 759

PubMed ID

  • 25168451

Pubmed Central ID

  • 25168451

Electronic International Standard Serial Number (EISSN)

  • 1097-6817

International Standard Serial Number (ISSN)

  • 0194-5998

Digital Object Identifier (DOI)

  • 10.1177/0194599814547497

Language

  • eng