A Quasi-3D compartmental multi-scale approach to detect and quantify diseased regional lung constriction using spirometry data.

Abstract

Spirometry is a widely used pulmonary function test to detect the airflow limitations associated with various obstructive lung diseases, such as asthma, chronic obstructive pulmonary disease, and even obesity-related complications. These conditions arise due to the change in the airway resistance, alveolar compliance, and inductance values. Currently, zero-dimensional compartmental models are commonly used for calibrating these resistance, compliance, and inductance values, ie, solving the inverse spirometry problem. However, zero-dimensional compartments cannot capture the flow physics or the spatial geometry effects, thereby generating a low fidelity prediction of the diseased lung. Computational fluid dynamics (CFD) models offer higher fidelity solutions but may be impractical for certain applications due to the duration of these simulations. Recently, a novel, fast-running, and robust Quasi-3D (Q3D) wire model for simulating the airflow in the human lung airway was developed by CFD Research Corporation. This Q3D method preserved the 3D spatial nature of the airways and was favorably validated against CFD solutions. In the present study, the Q3D compartmental multi-scale combination is further improved to predict regional lung constriction of diseased lungs using spirometry data. The Q3D mesh is resolved up to the eighth lung airway generation. The remainder of the airways and the alveoli sections are modeled using a compartmental approach. The Q3D geometry is then split into different spatial sections, and the resistance values in these regions are obtained using parameter inversion. Finally, the airway diameter values are then reduced to create the actual diseased lung model, corresponding to these resistance values. This diseased lung model can be used for patient-specific drug deposition predictions and the subsequent optimization of the orally inhaled drug products.