Imaging-based Clusters in Current Smokers of the COPD Cohort Associate with Clinical Characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Overview

Journal Respir Res

Specialty Pulmonary Medicine

Date 2018 Sep 20

PMID 30227877

Citations 15

Authors

Babak Haghighi

Sanghun Choi

Jiwoong Choi

Eric A Hoffman

Alejandro P Comellas

John D Newell Jr

R Graham Barr

Eugene Bleecker

Christopher B Cooper

David Couper

Mei Lan Han

Nadia N Hansel

Richard E Kanner

Ella A Kazerooni

Eric A C Kleerup

Fernando J Martinez

Wanda ONeal

Stephen I Rennard

Prescott G Woodruff

Ching-Long Lin

Affiliations

Soon will be listed here.

Abstract

Background: Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD.

Methods: Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers. Functional variables were derived from registration of TLC and RV images, e.g. functional small airways disease (fSAD%). Structural variables were assessed at TLC images, e.g. emphysema and airway wall thickness and diameter. We employed an unsupervised method for clustering.

Results: Four clusters were identified. Cluster 1 had relatively normal airway structures; Cluster 2 had an increase of fSAD% and wall thickness; Cluster 3 exhibited a further increase of fSAD% but a decrease of wall thickness and airway diameter; Cluster 4 had a significant increase of fSAD% and emphysema. Clinically, Cluster 1 showed normal FEV1/FVC and low exacerbations. Cluster 4 showed relatively low FEV1/FVC and high exacerbations. While Cluster 2 and Cluster 3 showed similar exacerbations, Cluster 2 had the highest BMI among all clusters.

Conclusions: Association of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations.

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