ArticleInternational journal of chronic obstructive pulmonary disease
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).
Article in International journal of chronic obstructive pulmonary disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01969344 (Subpopulations and Intermediate Outcome Measures in COPD Study), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Subpopulations and Intermediate Outcome Measures in COPD Study
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- Discriminative feature learning for multiclass lung disease classification using contrastive learning.Frontiers in radiology · 2026Article
- Assessment of ventilation heterogeneity and particle deposition in asthmatics using combined SPECT/CT imaging and computational modeling approaches.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025Article
- Longitudinal study of COPD phenotypes using integrated SPECT and qCT imaging.Frontiers in physiology · 2025Article
- Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Article
- A titratable murine model of progressive emphysema using tracheal porcine pancreatic elastase.Scientific reports · 2023Article
- Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.Medical physics · 2023Article
- COPD Phenotyping.Respiratory care · 2023Review
- A computed tomography imaging-based subject-specific whole-lung deposition model.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2022Article
- Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.Frontiers in physiology · 2022Article
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Authors and funding
26 authors.
Funding
Abstract
purposeQuantitative 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
methodsWe 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.
resultsCOPD 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.
conclusionqCT 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.
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