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)

Abstract

Background: Quantitative CT (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%), and preserved FEV1%predicted but impairment of FEV1/FVC ratio over one year. Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity, airway narrowing, and a reduced post-bronchodilator FEV1%predicted but preserved FEV1/FVC ratio 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.Conclusions: 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.

Chronic Obstructive Pulmonary Disease and Small Airways Diseases

“…It is unclear if QLI provides unique metrics that differ from the insights gained from clinical symptoms and pulmonary function testing. Longitudinal QLI has been able to identify specific subgroups at higher risk for progression of disease over time 54 and in this context might be helpful in identifying patients in need of more aggressive treatment and encouragement of smoking cessation.…”