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J Aerosol Med Pulm Drug Deliv. 2019 Mar 19. doi: 10.1089/jamp.2018.1487. [Epub ahead of print]

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

Author information

1 Department of Mechanical Engineering and The University of Iowa, Iowa City, Iowa.
2 IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa.
3 School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
4 Departments Radiology and The University of Iowa, Iowa City, Iowa.
5 Departments Occupational and Environmental Health, The University of Iowa, Iowa City, Iowa.
6 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
7 Departments of Internal Medicine and Pediatrics, Washington University School of Medicine, St. Louis, Missouri.
8 Department of Medical Physics and School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
9 Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
10 Division of Pulmonary Medicine and Critical Care, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
11 Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.



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.


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 Dh*(sLLL) (normalized airway hydraulic diameter in the left lower lobe), where reduced J(Total) and Dh*(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).


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 Dh*(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.


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.


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


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