title: Predicting postsurgery nasal physiology with computational modeling: current challenges and limitations.
abstract: BACKGROUND
  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.
  
  
  METHODS
  Prospective study.
  
  
  METHODS
  Academic medical center.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Otolaryngology-Head and Neck Surgery, Duke University Medical Center, Durham, North Carolina, USA dennis.frank@duke.edu.
authors.forename: Dennis O
authors.initials: DO
authors.lastname: Frank-Ito
authors.affiliation: Department of Otolaryngology/Head and Neck Surgery, University of North Carolina, Chapel Hill, North Carolina, USA.
authors.forename: Julia S
authors.initials: JS
authors.lastname: Kimbell
authors.affiliation: Division of Biostatistics, Institute for Health and Society, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
authors.forename: Purushottam
authors.initials: P
authors.lastname: Laud
authors.affiliation: Biotechnology & Bioengineering Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
authors.forename: Guilherme J M
authors.initials: GJ
authors.lastname: Garcia
authors.affiliation: Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
authors.forename: John S
authors.initials: JS
authors.lastname: Rhee
mesh_terms: D000328:Adult; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015508:Nasal Obstruction / Q000601:surgery*; D009666:Nose / Q000033:anatomy & histology* / Q000502:physiology*; D011379:Prognosis; D011446:Prospective Studies; D020127:Recovery of Function; D055815:Young Adult
keywords: computational fluid dynamics; nasal airway obstruction; predicting postsurgery nasal physiology; predominately obstructed side; transcribed surgery; virtual surgery