Subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)
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PROVIDER: PRJNA321233 | ENA |
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Project description:NHLBI TOPMed: Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
| PRJNA588107 | ENA
Project description:NHLBI TOPMed: Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
| PRJNA588108 | ENA
Project description:Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
| PRJNA835959 | ENA
Project description:Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
| PRJNA835960 | ENA
Project description:Subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)
| PRJNA321234 | ENA
Project description:Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) is a multicentre observational study of chronic obstructive pulmonary disease (COPD) designed to guide future development of therapies for COPD by providing robust criteria for subclassifying COPD participants into groups most likely to benefit from a given therapy during a clinical trial, and identifying biomarkers/phenotypes that can be used as intermediate outcomes to reliably predict clinical benefit during therapeutic trials. The goal is to enrol 3200 participants in four strata. Participants undergo a baseline visit and three annual follow-up examinations, with quarterly telephone calls. Adjudication of exacerbations and mortality will be undertaken.
| S-EPMC3954445 | biostudies-literature
Project description:BackgroundQuantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping.MethodsAn imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration.ResultsWe derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema.ConclusionsQCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.
| S-EPMC6631615 | biostudies-literature
Project description:BackgroundClassification 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.MethodsAmong 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.ResultsFour 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.ConclusionsAssociation of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations.
| S-EPMC6145340 | biostudies-literature
Project description:Rationale and objectivesThe longitudinal relationship between regional air trapping and emphysema remains unexplored. We have sought to demonstrate the utility of parametric response mapping (PRM), a computed tomography (CT)-based biomarker, for monitoring regional disease progression in chronic obstructive pulmonary disease (COPD) patients, linking expiratory- and inspiratory-based CT metrics over time.Materials and methodsInspiratory and expiratory lung CT scans were acquired from 89 COPD subjects with varying Global Initiative for Chronic Obstructive Lung Disease (GOLD) status at 30 days (n = 13) or 1 year (n = 76) from baseline as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) clinical trial. PRMs of CT data were used to quantify the relative volumes of normal parenchyma (PRM(Normal)), emphysema (PRM(Emph)), and functional small airways disease (PRM(fSAD)). PRM measurement variability was assessed using the 30-day interval data. Changes in PRM metrics over a 1-year period were correlated to pulmonary function (forced expiratory volume at 1 second [FEV1]). A theoretical model that simulates PRM changes from COPD was compared to experimental findings.ResultsPRM metrics varied by ∼6.5% of total lung volume for PRM(Normal) and PRM(fSAD) and 1% for PRM(Emph) when testing 30-day repeatability. Over a 1-year interval, only PRM(Emph) in severe COPD subjects produced significant change (19%-21%). However, 11 of 76 subjects showed changes in PRM(fSAD) greater than variations observed from analysis of 30-day data. Mathematical model simulations agreed with experimental PRM results, suggesting fSAD is a transitional phase from normal parenchyma to emphysema.ConclusionsPRM of lung CT scans in COPD patients provides an opportunity to more precisely characterize underlying disease phenotypes, with the potential to monitor disease status and therapy response.
| S-EPMC4289437 | biostudies-literature
Project description:Study Objectives:Sleep quality is poor among patients with chronic obstructive pulmonary disease (COPD), and studies show that sleep disturbance is associated with low overall quality of life in this population. We evaluated the impact of patient-reported sleep quality and sleep apnea risk on disease-specific and overall quality of life within patients with COPD enrolled in the SPIROMICS study, after accounting for demographics and COPD disease severity. Methods:Baseline data from 1341 participants [892 mild/moderate COPD (FEV1 ? 50% predicted); 449 severe COPD (FEV1 < 50%)] were used to perform three nested (blocks) regression models to predict quality of life (Short Form-12 mental and physical components and St. George's Respiratory Questionnaire). Dependent measures used for the nested regressions included the following: Block1: demographics and smoking history; Block 2: disease severity (forced expiratory volume 1 s; 6 min walk test); Block 3: risk for obstructive sleep apnea (OSA; Berlin questionnaire); and Block 4: sleep quality (Pittsburgh Sleep Quality Index [PSQI]). Results:Over half of participants with COPD reported poor sleep quality (Mean PSQI 6.4 ± 3.9; 50% with high risk score on the Berlin questionnaire). In all three nested regression models, sleep quality (Block 4) was a significant predictor of poor quality of life, over and above variables included in blocks 1-3. Conclusions:Poor sleep quality represents a potentially modifiable risk factor for poor quality of life in patients with COPD, over and above demographics and smoking history, disease severity, and risk for OSA. Improving sleep quality may be an important target for clinical interventions. Clinical Trial:SPIROMICS. Clinical Trial URL:http://www2.cscc.unc.edu/spiromics/. Clinical Trial Registration:ClinicalTrials.gov NCT01969344.
| S-EPMC5946919 | biostudies-literature