Computational Fluid Dynamics Modeling of Nasal Obstruction and Associations with Patient-Reported Outcomes

Background: Nasal obstruction is a common problem, with significant impact on quality of life. Accurate diagnosis may be challenging because of the complex and dynamic nature of the involved anatomy. Computational fluid dynamics modeling has the ability to identify specific anatomical defects, allowing for a targeted surgical approach. The goal of the current study is to better understand nasal obstruction as it pertains to disease-specific quality of life by way of a novel computational fluid dynamics model of nasal airflow. Methods: Fifty-three patients with nasal obstruction underwent computational fluid dynamics modeling based on computed tomographic imaging. Nasal resistance was compared to demographic data and baseline subjective nasal patency based on Nasal Obstructive Symptom Evaluation scores. Results: Mean Nasal Obstructive Symptom Evaluation score among all patients was 72.6. Nasal Obstructive Symptom Evaluation score demonstrated a significant association with nasal resistance in patients with static obstruction (p = 0.03). There was a positive correlation between Nasal Obstructive Symptom Evaluation score and nasal resistance in patients with static bilateral nasal obstruction (R-2 = 0.32) and poor correlation in patients with dynamic bilateral obstruction caused by nasal valve collapse (R-2 = 0.02). Patients with moderate and severe bilateral symptoms had significantly higher nasal resistance compared to those with unilateral symptoms (p = 0.048). Conclusions: Nasal obstruction is a multifactorial condition in most patients. This study shows correlation between simulated nasal resistance and Nasal Obstructive Symptom Evaluation score in a select group of patients. There is currently no standardized diagnostic algorithm or gold standard objective measure of nasal airflow; however, computational fluid dynamics may better inform treatment planning and surgical techniques on an individual basis.