Longitudinal imaging-based clusters in former smokers of the copd cohort associate with clinical characteristics: The subpopulations and intermediate outcome measures in copd study (spiromics)

Chunrui Zou, Frank Li, Jiwoong Choi, Babak Haghighi, Sanghun Choi, Prathish K. Rajaraman, Alejandro P. Comellas, John D. Newell, Chang Hyun Lee, R. Graham Barr, Eugene Bleecker, Christopher B. Cooper, David Couper, Meilan Han, Nadia N. Hansel, Richard E. Kanner, Ella A. Kazerooni, Eric C. Kleerup, Fernando J. Martinez, Wanda O’nealRobert Paine, Stephen I. Rennard, Benjamin M. Smith, Prescott G. Woodruff, Eirc A. Hoffman, Ching Long Lin

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. Patients and Methods: We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. Results: COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. Conclusion: qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

Original languageEnglish (US)
Pages (from-to)1477-1496
Number of pages20
JournalInternational Journal of COPD
Volume16
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • Computed tomography
  • Emphysema
  • Functional small airway disease
  • Longitudinal clustering

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Health Policy
  • Public Health, Environmental and Occupational Health

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