Esther Pompe1, Craig J Galbán2, Brian D Ross2, Leo Koenderman3, Nick Ht Ten Hacken4, Dirkje S Postma4, Maarten van den Berge4, Pim A de Jong5, Jan-Willem J Lammers3, Firdaus Aa Mohamed Hoesein5. 1. Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: e.pompe@umcutrecht.nl. 2. Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Center for Molecular Imaging, University of Michigan, Ann Arbor, MI, USA. 3. Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. 4. University of Groningen, University Medical Center Groningen, Department of Pulmonary Disease, Groningen, The Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands. 5. Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.
Abstract
BACKGROUND: In the search for specific phenotypes of chronic obstructive pulmonary disease (COPD) computed tomography (CT) derived Parametric Response Mapping (PRM) has been introduced. This study evaluates the association between PRM and currently available biomarkers of disease severity in COPD. METHODS: Smokers with and without COPD were characterized based on questionnaires, pulmonary function tests, body plethysmography, and low-dose chest CT scanning. PRM was used to calculate the amount of emphysema (PRMEmph) and non-emphysematous air trapping (i.e. functional small airway disease, PRMfSAD). PRM was first compared with other biomarkers for emphysema (Perc15) and air trapping (E/I-ratioMLD). Consequently, linear regression models were utilized to study associations of PRM measurements with clinical parameters. RESULTS: 166 participants were included with a mean ± SD age of 50.5 ± 17.7 years. Both PRMEmph and PRMfSAD were more strongly correlated with lung function parameters as compared to Perc15 and E/I-ratioMLD. PRMEmph and PRMfSAD were higher in COPD participants than non-COPD participants (14.0% vs. 1.1%, and 31.6% vs. 8.2%, respectively, both p < 0.001) and increased with increasing GOLD stage (all p < 0.001). Multivariate analysis showed that PRMfSAD was mainly associated with total lung capacity (TLC) (β = -7.90, p < 0.001), alveolar volume (VA) (β = 7.79, p < 0.001), and residual volume (β = 6.78, p < 0.001), whilst PRMEmph was primarily associated with Kco (β = 8.95, p < 0.001), VA (β = -6.21, p < 0.001), and TLC (β = 6.20, p < 0.001). CONCLUSIONS: PRM strongly associates with the presence and severity of COPD. PRM therefore appears to be a valuable tool in differentiating COPD phenotypes.
BACKGROUND: In the search for specific phenotypes of chronic obstructive pulmonary disease (COPD) computed tomography (CT) derived Parametric Response Mapping (PRM) has been introduced. This study evaluates the association between PRM and currently available biomarkers of disease severity in COPD. METHODS: Smokers with and without COPD were characterized based on questionnaires, pulmonary function tests, body plethysmography, and low-dose chest CT scanning. PRM was used to calculate the amount of emphysema (PRMEmph) and non-emphysematous air trapping (i.e. functional small airway disease, PRMfSAD). PRM was first compared with other biomarkers for emphysema (Perc15) and air trapping (E/I-ratioMLD). Consequently, linear regression models were utilized to study associations of PRM measurements with clinical parameters. RESULTS: 166 participants were included with a mean ± SD age of 50.5 ± 17.7 years. Both PRMEmph and PRMfSAD were more strongly correlated with lung function parameters as compared to Perc15 and E/I-ratioMLD. PRMEmph and PRMfSAD were higher in COPDparticipants than non-COPDparticipants (14.0% vs. 1.1%, and 31.6% vs. 8.2%, respectively, both p < 0.001) and increased with increasing GOLD stage (all p < 0.001). Multivariate analysis showed that PRMfSAD was mainly associated with total lung capacity (TLC) (β = -7.90, p < 0.001), alveolar volume (VA) (β = 7.79, p < 0.001), and residual volume (β = 6.78, p < 0.001), whilst PRMEmph was primarily associated with Kco (β = 8.95, p < 0.001), VA (β = -6.21, p < 0.001), and TLC (β = 6.20, p < 0.001). CONCLUSIONS: PRM strongly associates with the presence and severity of COPD. PRM therefore appears to be a valuable tool in differentiating COPD phenotypes.
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