Identifying Flow Variables That Best Correlate With Subjective Nasal Patency

Introduction: Nasal airway obstruction (NAO) is a subjective symptom commonly reported by patients, but objective measures are rarely used to quantify the severity of obstruction. Computational fluid dynamics (CFD) simulations of nasal airflow have the potential to improve surgical outcomes via virtual surgery planning, but it remains unknown which CFD variables are most predictive of subjective nasal patency.

Methods: CFD simulations of nasal airflow were performed in patient-specific models based on computed tomography (CT) scans of 27 patients with NAO and 47 healthy subjects at a steady-state flowrate of 15 L/min. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity and specificity of CFD variables to distinguish NAO patients from healthy individuals.

Results: Many anatomic and unilateral CFD variables were statistically different between NAO patients and healthy subjects, including the airspace minimal cross-sectional area, flowrate in the most obstructed side (MOS), resistance in the MOS, heat flux in the MOS, and regional airflow distribution (inferior, middle, and superior) in coronal sections in the turbinate region of the MOS. The CFD variable with the best diagnostic performance to distinguish NAO patients from healthy subjects was the middle airflow in a coronal section in the turbinate region with a sensitivity of 89% and specificity of 83%.

Conclusions: Our results suggest that middle airflow plays an important role in the perception of nasal airflow, possibly by activating a larger number of TRPM8 cold receptors. The high sensitivity and specificity of CFD variables to distinguish NAO patients from healthy subjects supports the future application of CFD models in NAO surgical planning.