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Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()

BACKGROUND: Asthma heterogeneity is multidimensional and requires additional tools to unravel its complexity. Computed tomography (CT)–assessed proximal airway remodeling and air trapping in asthmatic patients might provide new insights into underlying disease mechanisms. OBJECTIVES: The aim of this...

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Autores principales: Gupta, Sumit, Hartley, Ruth, Khan, Umair T., Singapuri, Amisha, Hargadon, Beverly, Monteiro, William, Pavord, Ian D., Sousa, Ana R., Marshall, Richard P., Subramanian, Deepak, Parr, David, Entwisle, James J., Siddiqui, Salman, Raj, Vimal, Brightling, Christopher E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Mosby 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3969578/
https://www.ncbi.nlm.nih.gov/pubmed/24238646
http://dx.doi.org/10.1016/j.jaci.2013.09.039
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author Gupta, Sumit
Hartley, Ruth
Khan, Umair T.
Singapuri, Amisha
Hargadon, Beverly
Monteiro, William
Pavord, Ian D.
Sousa, Ana R.
Marshall, Richard P.
Subramanian, Deepak
Parr, David
Entwisle, James J.
Siddiqui, Salman
Raj, Vimal
Brightling, Christopher E.
author_facet Gupta, Sumit
Hartley, Ruth
Khan, Umair T.
Singapuri, Amisha
Hargadon, Beverly
Monteiro, William
Pavord, Ian D.
Sousa, Ana R.
Marshall, Richard P.
Subramanian, Deepak
Parr, David
Entwisle, James J.
Siddiqui, Salman
Raj, Vimal
Brightling, Christopher E.
author_sort Gupta, Sumit
collection PubMed
description BACKGROUND: Asthma heterogeneity is multidimensional and requires additional tools to unravel its complexity. Computed tomography (CT)–assessed proximal airway remodeling and air trapping in asthmatic patients might provide new insights into underlying disease mechanisms. OBJECTIVES: The aim of this study was to explore novel, quantitative, CT-determined asthma phenotypes. METHODS: Sixty-five asthmatic patients and 30 healthy subjects underwent detailed clinical, physiologic characterization and quantitative CT analysis. Factor and cluster analysis techniques were used to determine 3 novel, quantitative, CT-based asthma phenotypes. RESULTS: Patients with severe and mild-to-moderate asthma demonstrated smaller mean right upper lobe apical segmental bronchus (RB1) lumen volume (LV) in comparison with healthy control subjects (272.3 mm(3) [SD, 112.6 mm(3)], 259.0 mm(3) [SD, 53.3 mm(3)], 366.4 mm(3) [SD, 195.3 mm(3)], respectively; P = .007) but no difference in RB1 wall volume (WV). Air trapping measured based on mean lung density expiratory/inspiratory ratio was greater in patients with severe and mild-to-moderate asthma compared with that seen in healthy control subjects (0.861 [SD, 0.05)], 0.866 [SD, 0.07], and 0.830 [SD, 0.06], respectively; P = .04). The fractal dimension of the segmented airway tree was less in asthmatic patients compared with that seen in control subjects (P = .007). Three novel, quantitative, CT-based asthma clusters were identified, all of which demonstrated air trapping. Cluster 1 demonstrates increased RB1 WV and RB1 LV but decreased RB1 percentage WV. On the contrary, cluster 3 subjects have the smallest RB1 WV and LV values but the highest RB1 percentage WV values. There is a lack of proximal airway remodeling in cluster 2 subjects. CONCLUSIONS: Quantitative CT analysis provides a new perspective in asthma phenotyping, which might prove useful in patient selection for novel therapies.
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spelling pubmed-39695782014-03-31 Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients() Gupta, Sumit Hartley, Ruth Khan, Umair T. Singapuri, Amisha Hargadon, Beverly Monteiro, William Pavord, Ian D. Sousa, Ana R. Marshall, Richard P. Subramanian, Deepak Parr, David Entwisle, James J. Siddiqui, Salman Raj, Vimal Brightling, Christopher E. J Allergy Clin Immunol Asthma and Lower Airway Disease BACKGROUND: Asthma heterogeneity is multidimensional and requires additional tools to unravel its complexity. Computed tomography (CT)–assessed proximal airway remodeling and air trapping in asthmatic patients might provide new insights into underlying disease mechanisms. OBJECTIVES: The aim of this study was to explore novel, quantitative, CT-determined asthma phenotypes. METHODS: Sixty-five asthmatic patients and 30 healthy subjects underwent detailed clinical, physiologic characterization and quantitative CT analysis. Factor and cluster analysis techniques were used to determine 3 novel, quantitative, CT-based asthma phenotypes. RESULTS: Patients with severe and mild-to-moderate asthma demonstrated smaller mean right upper lobe apical segmental bronchus (RB1) lumen volume (LV) in comparison with healthy control subjects (272.3 mm(3) [SD, 112.6 mm(3)], 259.0 mm(3) [SD, 53.3 mm(3)], 366.4 mm(3) [SD, 195.3 mm(3)], respectively; P = .007) but no difference in RB1 wall volume (WV). Air trapping measured based on mean lung density expiratory/inspiratory ratio was greater in patients with severe and mild-to-moderate asthma compared with that seen in healthy control subjects (0.861 [SD, 0.05)], 0.866 [SD, 0.07], and 0.830 [SD, 0.06], respectively; P = .04). The fractal dimension of the segmented airway tree was less in asthmatic patients compared with that seen in control subjects (P = .007). Three novel, quantitative, CT-based asthma clusters were identified, all of which demonstrated air trapping. Cluster 1 demonstrates increased RB1 WV and RB1 LV but decreased RB1 percentage WV. On the contrary, cluster 3 subjects have the smallest RB1 WV and LV values but the highest RB1 percentage WV values. There is a lack of proximal airway remodeling in cluster 2 subjects. CONCLUSIONS: Quantitative CT analysis provides a new perspective in asthma phenotyping, which might prove useful in patient selection for novel therapies. Mosby 2014-03 /pmc/articles/PMC3969578/ /pubmed/24238646 http://dx.doi.org/10.1016/j.jaci.2013.09.039 Text en © 2013 The Authors http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Asthma and Lower Airway Disease
Gupta, Sumit
Hartley, Ruth
Khan, Umair T.
Singapuri, Amisha
Hargadon, Beverly
Monteiro, William
Pavord, Ian D.
Sousa, Ana R.
Marshall, Richard P.
Subramanian, Deepak
Parr, David
Entwisle, James J.
Siddiqui, Salman
Raj, Vimal
Brightling, Christopher E.
Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title_full Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title_fullStr Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title_full_unstemmed Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title_short Quantitative computed tomography–derived clusters: Redefining airway remodeling in asthmatic patients()
title_sort quantitative computed tomography–derived clusters: redefining airway remodeling in asthmatic patients()
topic Asthma and Lower Airway Disease
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3969578/
https://www.ncbi.nlm.nih.gov/pubmed/24238646
http://dx.doi.org/10.1016/j.jaci.2013.09.039
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