Evaluation of human obstructive sleep apnea using computational fluid dynamics

Commun Biol. 2019 Nov 21:2:423. doi: 10.1038/s42003-019-0668-z. eCollection 2019.

Abstract

Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD) simulations. The CT scans of seven adult patients diagnosed with OSA were used to reconstruct the 3D models of the upper airways and CFD modeling and analyses were performed. Results from the fluid simulations were compared with the apnea-hypopnea index. Here we show a correlation between a CFD-based parameter, the adjusted pressure coefficient (Cp*), and the respective apnea-hypopnea index (Pearson's r = 0.91, p = 0.004), which suggests that the anatomical-based model coupled with CFD could provide functional and localized information for different regions of the upper airways.

Keywords: Computational models; Diagnostic markers; Translational research.

MeSH terms

  • Algorithms
  • Biomarkers*
  • Humans
  • Imaging, Three-Dimensional
  • Models, Biological*
  • Polysomnography
  • Severity of Illness Index
  • Sleep Apnea, Obstructive / diagnosis*
  • Sleep Apnea, Obstructive / etiology
  • Tomography, X-Ray Computed
  • Translational Research, Biomedical
  • Workflow

Substances

  • Biomarkers