Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

• Published in: Respiratory research 2019-07, Vol.20 (1), p.153-14, Article 153
• Main Authors: Haghighi, Babak, Choi, Sanghun, Choi, Jiwoong, Hoffman, Eric A, Comellas, Alejandro P, Newell, Jr, John D, Lee, Chang Hyun, Barr, R Graham, Bleecker, Eugene, Cooper, Christopher B, Couper, David, Han, Mei Lan, Hansel, Nadia N, Kanner, Richard E, Kazerooni, Ella A, Kleerup, Eric A C, Martinez, Fernando J, O'Neal, Wanda, Paine, 3rd, Robert, Rennard, Stephen I, Smith, Benjamin M, Woodruff, Prescott G, Lin, Ching-Long
• Format: Article
• Language: English
• Subjects: Aged; Chronic obstructive lung disease; Cohort Studies; COPD; Cross-Sectional Studies; Demographic aspects; Diagnosis; Emphysema; Female; Forced Expiratory Volume; Former smokers; Functional small airway disease; Health aspects; Humans; Imaging, Three-Dimensional - methods; Imaging-based cluster analysis; Male; Medical examination; Middle Aged; Outcome Assessment, Health Care; Pulmonary Disease, Chronic Obstructive - diagnostic imaging; Pulmonary Disease, Chronic Obstructive - epidemiology; Pulmonary Disease, Chronic Obstructive - physiopathology; Risk factors; Smokers; Smoking; Smoking - epidemiology; Smoking - physiopathology; Tomography, X-Ray Computed - methods
• ISSN: 1465-993X; 1465-9921; 1465-993X
• DOI: 10.1186/s12931-019-1121-z

Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping. An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration. We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema. QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.