Quantitative assessment of the upper airway in infants and children with subglottic stenosis

Abstract

OBJECTIVES: Determine whether geometric measures and computational fluid dynamic modeling (CFD) derived from medical imaging are effective diagnostic and treatment planning tools for pediatric subglottic stenosis (SGS). STUDY DESIGN: Retrospective chart and imaging review. SETTING: Tertiary Care Hospital SUBJECTS AND METHODS: CT scans of children (n=17) with SGS were analyzed by geometric and (CFD) methods. Polysomnograms (n=15) were also analyzed. CT’s were also analyzed by age/weight flow normalization and comparison to an Atlas created from normal CT’s. Five geometric, seven CFD, and five PSG measures were analyzed to determine their correlation with which patients received surgery subsequent to the CT/PSG dataset versus those who did not. Statistical analysis was performed using a two-sample t-test with Bonferroni correction and area under the curve analysis. RESULTS: Two geometric indices and one CFD measure were significant for determining which children with SGS received surgery. Polysomnography was less helpful in this determination. Optimal cutoffs for these values were determined from this dataset. CONCLUSIONS: A number of geometric and CFD variables were sensitive at determining which patients with SGS received surgical intervention versus those who did not. Polysomnography was less helpful in making this determination. Discrete, quantitative assessment of the pediatric airway was performed, yielding preliminary data regarding possible objective thresholds for surgical versus non-surgical treatment of disease. This study is limited by its small, retrospective, single institution nature; further studies to validate these findings and possibly optimize treatment threshold recommendations are warranted.

Publication
The Laryngoscope
Yi Hong
Yi Hong
Ph.D. in Computer Science
Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.

Related