Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD
Abstract Background Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated. Re...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | Respiratory Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12931-023-02318-4 |
| _version_ | 1797945733048958976 |
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| author | Kenneth Verstraete Nilakash Das Iwein Gyselinck Marko Topalovic Thierry Troosters James D. Crapo Edwin K. Silverman Barry J. Make Elizabeth A. Regan Robert Jensen Maarten De Vos Wim Janssens |
| author_facet | Kenneth Verstraete Nilakash Das Iwein Gyselinck Marko Topalovic Thierry Troosters James D. Crapo Edwin K. Silverman Barry J. Make Elizabeth A. Regan Robert Jensen Maarten De Vos Wim Janssens |
| author_sort | Kenneth Verstraete |
| collection | DOAJ |
| description | Abstract Background Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated. Research question We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants. Study design and methods In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD. Results Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R2 was higher in moderate-severe COPD, while in mild COPD, the adjusted R2 for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT. Interpretation The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD. |
| first_indexed | 2024-04-10T20:59:48Z |
| format | Article |
| id | doaj.art-505c03cc8c7c4fa39e9185017f45941b |
| institution | Directory Open Access Journal |
| issn | 1465-993X |
| language | English |
| last_indexed | 2024-04-10T20:59:48Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj.art-505c03cc8c7c4fa39e9185017f45941b2023-01-22T12:22:45ZengBMCRespiratory Research1465-993X2023-01-0124111210.1186/s12931-023-02318-4Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPDKenneth Verstraete0Nilakash Das1Iwein Gyselinck2Marko Topalovic3Thierry Troosters4James D. Crapo5Edwin K. Silverman6Barry J. Make7Elizabeth A. Regan8Robert Jensen9Maarten De Vos10Wim Janssens11Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU LeuvenLaboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU LeuvenLaboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU LeuvenArtiQ NVDepartment of Rehabilitation Sciences, KU LeuvenNational Jewish Medical and Research CenterChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical SchoolNational Jewish Medical and Research CenterNational Jewish Medical and Research CenterUniversity of UtahSTADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU LeuvenLaboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU LeuvenAbstract Background Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated. Research question We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants. Study design and methods In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD. Results Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R2 was higher in moderate-severe COPD, while in mild COPD, the adjusted R2 for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT. Interpretation The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD.https://doi.org/10.1186/s12931-023-02318-4COPDComputed tomographyMaximal expiratory flow-volume curvePrincipal component analysis |
| spellingShingle | Kenneth Verstraete Nilakash Das Iwein Gyselinck Marko Topalovic Thierry Troosters James D. Crapo Edwin K. Silverman Barry J. Make Elizabeth A. Regan Robert Jensen Maarten De Vos Wim Janssens Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD Respiratory Research COPD Computed tomography Maximal expiratory flow-volume curve Principal component analysis |
| title | Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD |
| title_full | Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD |
| title_fullStr | Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD |
| title_full_unstemmed | Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD |
| title_short | Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD |
| title_sort | principal component analysis of flow volume curves in copdgene to link spirometry with phenotypes of copd |
| topic | COPD Computed tomography Maximal expiratory flow-volume curve Principal component analysis |
| url | https://doi.org/10.1186/s12931-023-02318-4 |
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