Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1782309280106414080 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3969578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Mosby |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT guptasumit quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT hartleyruth quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT khanumairt quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT singapuriamisha quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT hargadonbeverly quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT monteirowilliam quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT pavordiand quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT sousaanar quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT marshallrichardp quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT subramaniandeepak quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT parrdavid quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT entwislejamesj quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT siddiquisalman quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT rajvimal quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients AT brightlingchristophere quantitativecomputedtomographyderivedclustersredefiningairwayremodelinginasthmaticpatients |