The inspiratory and expiratory CT based COPD phenotypes using cluster analysis

Introduction and Objectives: Disease Probability Measure (DPM) is a useful image assessment tool for gas-trapping and emphysema using inspiratory and expiratory computed tomography (CT) data for chronic obstructive pulmonary disease (COPD). We hypothesized that the cluster analysis using DPM parameters could help to understand the relationship between the pathological changes of the lungs and other parameters in COPD. Methods: A total of 131 COPD patients were enrolled. Forced oscillation technique (FOT), spirometry and inspiratory and expiratory CT scans were conducted. Using CT data, the percentage of low attenuation voxels (LAV%) and the square root of airway wall area of the hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) were quantitatively evaluated. DPM analysis was performed and hierarchical cluster analysis was applied. The COPD patients were classified into five imaging clusters using DPM parameters; DPMNormal, DPMGasTrap and DPMEmphy. The clusters were named as follows; Normal (NL), Normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM) and Emphysema (EM). Results: FEV1%predicted and respiratory reactance at 5Hz (X5) were gradually decreased in the order of the NL, NL-GT, GT, GT-EM, and EM. LAV% was significantly higher in the GT-EM than in the NL, NL-GT and GT. LAV% was also significantly higher in the EM than in the GT-EM. Four clusters other than the NL showed significantly higher values of √Aaw at Pi10 than the NL cluster but there were no significant differences among the four clusters. Conclusions: The five DPM clusters could reflect the pathophysiological changes in COPD.