Differences in Particle Deposition Between Members of Imaging-Based Asthma Clusters

Abstract

Background: Four computed tomography (CT) imaging-based clusters have been identified in a study of the Severe Asthma Research Program (SARP) cohort and have been significantly correlated with clinical and demographic metrics (J Allergy Clin Immunol 2017; 140:690-700.e8). We used a computational fluid dynamics (CFD) model to investigate air flow and aerosol deposition within imaging archetypes representative of the four clusters. Methods: CFD simulations for air flow and 1-8 μm particle transport were performed using CT-based airway models from two healthy subjects and eight asthma subjects. The subject selection criterion was based on the discriminant imaging-based flow-related variables of J(Total) (average local volume expansion in the total lung) and D_h*(sLLL) (normalized airway hydraulic diameter in the left lower lobe), where reduced J(Total) and D_h*(sLLL) indicate reduced regional ventilation and airway constriction, respectively. The analysis focused on the comparisons between all clusters with respect to healthy subjects, between cluster 2 and cluster 4 (nonsevere and severe asthma clusters with airway constriction) and between cluster 3 and cluster 4 (two severe asthma clusters characterized by normal and constricted airways, respectively). Results: Nonsevere asthma cluster 2 and severe asthma cluster 4 subjects characterized by airway constriction had an increase in the deposition fraction (DF) in the left lower lobe. Constricted flows impinged on distal bifurcations resulting in large depositions. Although both cluster 3 (without constriction) and cluster 4 (with constriction) were severe asthma, they exhibited different particle deposition patterns with increasing particle size. The statistical analysis showed that D_h*(sLLL) plays a more important role in particle deposition than J(Total), and regional flow fraction is correlated with DF among lobes for smaller particles. Conclusions: We demonstrated particle deposition characteristics associated with cluster-specific imaging-based metrics such as airway constriction, which could pertain to the design of future drug delivery improvements.

Keywords: airway constriction; cluster analysis; computational fluid dynamics; inhaled corticosteroid; particle deposition; quantitative computed tomography.

FIG. 1.

CT-based 3D–1D geometry of HM subject with the five lobes and major branches labeled. The colored lines distinguish the five lobes and represent the 1D skeleton that maps out the airway branching patterns and provides branching angles, lengths, and average diameters for 3D geometry construction. The region highlighted by a red circle references the lung location of Figure 4. 1D, one-dimensional; 3D, three-dimensional; CT, computed tomography; HM, healthy male; LMB, left main bronchus; LLL, left lower lobe; LUL, left upper lobe; RLL, right lower lobe; RMB, right main bronchus; RML, right middle lobe; RUL, right upper lobe.

FIG. 2.

Regional DF for (a) 1, (b) 2, (c) 4, and (d) 8 μm diameter particles. Plots are separated by lobe and cluster. DF for all five lobes between clusters was significantly different (p < 0.05). DF, deposition fraction; HF, healthy female; HM, healthy male; LLL, left lower lobe; LUL, left upper lobe; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe.

FIG. 3.

Mean deposition fractions of 1, 2, 4, and 8 μm particles in (a) LLL and (b) all the lobes for the three C3 (blank) and C4 (filled) subjects, respectively. Error bars indicate standard deviations.

FIG. 4.

Average DD plots of LLB for (a) C4, (b) HM, (c) C2, and (d) C3. DD presented as number of particles (N) per mm². Plots associated with 4 μm diameter particle simulations. HM and C4 subset figures show iso-surfaces of air speed at 2.5 m/s (green) and 5 m/s (brown) with final deposition locations at LLB and child branches. See Figure 1a for reference of the LLB location in the lung of these figures. DD, deposition density.

FIG. 5.

Projection of the four color-coded cluster subjects and their respective cluster means (“x”) on PC 1 and PC 2 coordinates. PC, principal component.