Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes - 06/09/17
for the
National Heart, Lung and Blood Institute's Severe Asthma Research Programj
Abstract |
Background |
Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects.
Objective |
The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics.
Methods |
We performed an imaging-based cluster analysis using quantitative computed tomography–based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables.
Results |
Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively.
Conclusions |
Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Key words : Computed tomography, image processing, severe asthma, air trapping, image registration, luminal narrowing, wall thickening, airway circularity, cluster analysis, neutrophilic asthma
Abbreviations used : ACQ, ACT, ADI, AirT%, AQLQ, BAL, BMI, BronInt, Cr, Dh, FRC, ICS, LA, OCS, PCA, PFT, QCT, RMB, SARP, sLLL, sLUL, sRLL, sRML, sRUL, TA, θ, TLC, ΔVairF, WA%, WT
Plan
| Supported in part by National Institutes of Health grants: U01 HL114494, HL109152; R01 HL094315, HL112986, HL69174, HL064368, HL091762, HL069116; S10 RR022421; U10 HL109257, HL109168; UL1 RR024153 (CTSI), UL1 TR000448, UL1 TR000427 (CTSA). We thank J. Choi, M. J. Escher and A. M. Thompson for assisting with data analysis and acquisition, and SARP coordinators and patients for their contribution. |
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| Disclosure of potential conflict of interest: S. Choi, M. L. Schiebler, and C.-L. Lin receive grant support from the National Heart, Lung, and Blood Institute. E. A. Hoffman receives grant support from the National Institutes of Health (NIH) and is a founder and shareholder for VIDA Diagnostics. S. E. Wenzel serves as a consultant for Novartis, Knopp, GlaxoSmithKline, AstraZeneca, Sanofi, Genentech, Boehringer Ingelheim, and Circassia. M. Castro serves as a consultant for Boston Scientific, NeoStem, and Holaira; serves as paid speaker to Genentech; receives grant support from Amgen, Teva, Novartis, GlaxoSmithKline, Sanofi Aventis, Vectura, Medimmune, Invion and Boehringer Ingelheim; received royalties from Elsevier; and holds stock in Sparo. S. Fain receives grant support from GE Healthcare and the NIH. N. Jarjour receives grant support from the NIH and serves as a consultant for Teva Pharmaceuticals, AstraZeneca, and Daiichi Sankyo. The rest of the authors declare that they have no relevant conflicts of interest. |
Vol 140 - N° 3
P. 690 - septembre 2017 Retour au numéro
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