Relationship between degree of obstruction and airflow limitation in subglottic stenosis

Objectives

Subglottic stenosis (SGS) is one of the most common airway disorders in pediatric patients. Currently, treatment decisions rely primarily on the Cotton-Myer scale, which classifies SGS severity based on percentage reduction in airspace cross-sectional area (CSA). However, the precise relationship between upper airway resistance and subglottic CSA is unknown. We hypothesize that airway resistance can be described by the Bernoulli Obstruction Theory, which predicts that airway resistance is inversely proportional to airspace CSA ( ) in cases of severe constriction.

Methods

Computed tomography (CT) scans of six healthy subjects and five SGS patients were used to create three-dimensional models of the respiratory tract from nostrils to carina. Cylindrical segments of varying lengths and varying diameters were digitally inserted in the subglottis of the healthy subjects to create simulated SGS models. Computational fluid dynamics simulations were run, and airway resistance was computed in the simulated SGS models and actual SGS models.

Results

Constriction diameter had a greater impact in airway resistance than constriction length. In agreement with the Bernoulli Obstruction Theory, airway resistance in the simulated SGS models was well represented by the power law , where is a constant and the exponent b ranged from −0.85 to −1.07. The percentage reduction in airflow at a constant pressure drop was found to be directly proportional to the percentage reduction in CSA in the limit of severe constrictions, namely , where . Airway resistances in the simulated SGS models were similar to resistances in models based on CT scans of actual SGS patients, suggesting that our simulated SGS models were representative of airway resistance in actual SGS patients.

Conclusion

Our computer simulations suggest that the degree of airflow limitation in SGS patients may be estimated based on anatomic measurements alone. Future studies are recommended to test these predictions in larger cohorts.

Level of Evidence

4. Laryngoscope, 2017

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