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,1,2 Frank Li,2,3 Jiwoong Choi,1,4 Babak Haghighi,5 Sanghun Choi,6 Prathish K Rajaraman,1,2 Alejandro P Comellas,7 John D Newell Jnr,8 Chang Hyun Lee,8,9 R Graham Barr,10 Eugene Bleecker,11 Christopher B Cooper,12 David Couper,13 Meilan Han,14 Nadia N Hansel,15 Richard E Kanner,16 Ella A Kazerooni,17 Eric C Kleerup,18 Fernando J Martinez,19 Wanda O’Neal,20 Robert Paine III,16 Stephen I Rennard,21 Benjamin M Smith,22,23 Prescott G Woodruff,24 Eirc A Hoffman,3,7,8 Ching-Long Lin1– 3,8 1Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA; 2IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA; 3Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA; 4Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA; 5Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 6School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea; 7Department of Internal Medicine, University of Iowa, Iowa City, IA, USA; 8Department of Radiology, University of Iowa, Iowa City, IA, USA; 9Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; 10Mailman School of Public Health, Columbia University, New York, NY, USA; 11Department of Medicine, The University of Arizona, Tucson, AZ, USA; 12Department of Physiology, UCLA, Los Angeles, CA, USA; 13Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA; 14Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; 15School of Medicine, Johns Hopkins, Baltimore, MD, USA; 16School of Medicine, University of Utah, Salt Lake City, UT, USA; 17Department of Radiology, University of Michigan, Ann Arbor, MI, USA; 18Department of Medicine, UCLA, Los Angeles, CA, USA; 19Weill Cornell Medicine, Cornell University, New York, NY, USA; 20School of Medicine, University of North Carolina, Chapel Hill, NC, USA; 21Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA; 22Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA; 23Department of Medicine, McGill University Health Centre Research Institute, Montreal, Canada; 24Department of Medicine, University of California at San Francisco, San Francisco, CA, USACorrespondence: Ching-Long Lin 2406 Seamans Center for the Engineering Art and Science, Iowa City, IA, 52242, USATel +1 319 335 5673Email ching-long-lin@uiowa.eduPurpose: 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.Keywords: computed tomography, emphysema, functional small airway disease, longitudinal clustering