Parametric response mapping on chest computed tomography associates with clinical and functional parameters in chronic obstructive pulmonary disease

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...

Full description

Saved in:
Bibliographic Details
Published inRespiratory medicine Vol. 123; pp. 48 - 55
Main Authors Pompe, Esther, Galbán, Craig J, Ross, Brian D, Koenderman, Leo, ten Hacken, Nick HT, Postma, Dirkje S, van den Berge, Maarten, de Jong, Pim A, Lammers, Jan-Willem J, Mohamed Hoesein, Firdaus AA
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.02.2017
Elsevier Limited
Subjects
Age
RV
PRM
TLC
BMI
VA
VC
CT
FVC
KCO
MRC
Online AccessGet full text

Cover

Loading…
More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0954-6111
1532-3064
DOI:10.1016/j.rmed.2016.11.021