Robust nondimensional estimators to assess the nasal airflow in health and disease.

Abstract

There are significant variations of both human nose shapes and airflow patterns inside nasal cavities, so it is difficult to provide a comprehensive medical identification using a universal template for what otolaryngologists consider normal breathing at rest. In addition, airflow patterns present even more random characteristics in diseased nasal cavities. To give a medical assessment to differentiate the nasal cavities in health and disease, we propose 2 nondimensional estimators obtained from both medical images and computational fluid dynamics. The first mathematical estimator ϕ is a function of geometric features and potential asymmetries between nasal passages, while the second estimator R represents in fluid mechanics terms the total nasal resistance that corresponds to the atmosphere-choana pressure drop. These estimators only require global information such as nasal geometry and magnitudes of flow determined by simulations under laminar conditions. We find that these estimators take low and high values for healthy and diseased nasal cavities, respectively. Our study, based on 24 healthy and 25 diseased Caucasian subjects, reveals that there is an interval of values associated with healthy cavities that clusters in a small region of the plane ϕ-R. Therefore, these estimators can be seen as a first approximation to provide nasal airflow data to the clinician in a noninvasive method, as the computed tomography scan that provides the required images is routinely obtained as a result of the preexisting naso-sinusal condition.