Computational fluid dynamics of small airway disease in chronic obstructive pulmonary disease

Abstract

Background: Small airways (<2 mm diameter) are major sites of airflow obstruction in chronic obstructive pulmonary disease (COPD). This study aimed to quantify the impact of small airway disease, characterized by narrowing, occlusion, and obliteration, on airflow parameters in smokers and end-stage patients with COPDs.

Methods: We performed computational fluid dynamics (CFD) simulations of inspiratory airflow in three lung groups: control non-used donor lungs (no smoking/emphysema history), non-used donor lungs with a smoking history and emphysema, and explanted end-stage COPD lungs. Each group included four lungs, with two tissue cylinders. Micro-CT-scanned small airways were segmented into 3D models for CFD simulations to quantify pressure, resistance, and shear stress. CFD results were benchmarked against simplified linear and Weibel models.

Findings: CFD simulations showed higher pressures in COPD vs. controls (p = 0.0091) and smokers (p = 0.015), along with increased resistance (p = 0.0057 vs. controls; p = 0.0083 vs. smokers) and up to a tenfold rise in shear stress (p = 0.010 vs. controls). Narrowing and occlusion were shown to independently increase pressure, resistance, and shear stress, which were validated through segmentation corrections. Pressures and resistance assessed with simplified models were up to seven-fold higher for smokers and even 72 higher for COPD compared with CFD values.

Interpretation: These findings show that increased airflow parameters can explain the association between small airway disease and airflow limitation in COPD, underscoring small airway vulnerability. Additionally, they highlight the limitations of theoretical models in accurately capturing small airway disease.

Funding: Supported by the KU Leuven (C16/19/005).

Keywords: Airway remodelling; Airway resistance; Chronic obstructive pulmonary disease (COPD); Computational fluid dynamics (CFD); Obstruction; Small airway disease.

Fig. 1

Procedure for airflow modelling of small airways: inflated lungs (a) were sectioned into 2 cm slices, from which 2 tissue cylinders/lung with a diameter of 1.4 cm were selected. Micro-CT scans of these cylinders (b) were used to segment and analyse small airway disease. The segmented airways were further analysed (c) and converted into three-dimensional (3D) computer-aided design (CAD) models (d) for computational fluid dynamics (CFD) simulations. A computational mesh was then constructed from the CAD model (e), defining boundary conditions for the inlet and terminal (outlet) branches. CFD simulations (f) quantified flow parameters, including pressure, velocity, and shear stress within the small airways. (g) Simplified linear models, including the Weibel symmetrical model, were derived from the CAD models by assigning micro-CT-based diameters and lengths to each airway branch for airflow simulation. For the Weibel symmetrical model, the number of branches per model was maintained, while the length, diameter, and symmetry were aligned with Weibel data.

Fig. 2

Comparison of inlet air pressure (a), total small airway resistance (b), and wall shear stress (c) across controls, smokers, and COPD groups. ANOVA showed significant increases in airway pressure (p = 0.015∗ and p = 0.009∗∗, respectively) and small airway resistance (p = 0.0082∗∗ and p = 0.0047∗∗, respectively) in the COPD group compared to smokers and controls. Wall shear stress was significantly higher in the COPD group compared to controls (p = 0.01∗). No significant differences were observed between smokers and controls for any parameter (p > 0.05). Data represents statistical comparisons (n = 8 per group). Small airways from each group are shown, visualizing pressure (d) and wall shear stress contours (e).

Fig. 3

CFD simulation of an airway narrowing. A segmentation of small airways from a COPD lung with a narrowing at the generation of the inlet (a). A CAD model was generated (model I) together with a corrected model where the narrowing was removed (model II) (b). Pressure contours (c) are illustrated for the narrowed and corrected models. Velocity contours and gradients of sequential cross-sections highlight the velocity gradients around the narrowed region (d). Wall shear stress contours (e) with magnification on the constricted region to highlight the impact of increased velocity gradients.

Fig. 4

CFD simulation of a small airway occlusion. A segmentation of a small airway with an occlusion at one generation down from the inlet branch marked by a red circle and a detailed microCT image is the accompanying red circle originating from a COPD lung (a). An uncorrected (model I) and corrected (model II) CAD model for this occlusion were generated (b) to illustrate and quantify the impact on pressure (c, e), wall shear stress (d, e), and resistance (e).

Fig. 5

CFD simulation of multiple occlusions. A small airway segmentation from a smoker lung included three occlusions marked by white arrows (a). CAD models (b) from the uncorrected (model IV) to the fully corrected model (model I) were used for CFD simulation. Contours of pressure (c) and wall shear stress (d) visualizing the impact of progression of small airway disease. Quantification of pressure, wall shear stress, and resistance demonstrated the impact of the multiple occlusions (e).

Fig. 6

Comparative analysis of the diverse computational models of small airways: (a) Comparison of small airway diameters derived from micro-CT with corresponding Weibel-A model values across different generations in control, smoker, and COPD samples, based on a representative core from each group (b) Pressure and (c) resistance comparison between the 3D, linear, and Weibel models for control, smoker, and COPD airways.