The purposes of this study were: to describe chest CT findings in normal non-smoking controls and cigarette smokers with and without COPD; to compare the prevalence of CT abnormalities with severity of COPD; and to evaluate concordance between visual and quantitative chest CT (QCT) scoring Methods Volumetric inspiratory and expiratory CT scans of 294 subjects, including normal non-smokers, smokers without COPD, and smokers with GOLD Stage I-IV COPD, were scored at a multi-reader workshop using a standardized worksheet. There were fifty-eight observers (33 pulmonologists, 25 radiologists); each scan was scored by 9–11 observers. Interobserver agreement was calculated using kappa statistic. Median score of visual observations was compared with QCT measurements. Results Interobserver agreement was moderate for the presence or absence of emphysema and for the presence of panlobular emphysema; fair for the presence of centrilobular, paraseptal, and bullous emphysema subtypes and for the presence of bronchial wall thickening; and poor for gas trapping, centrilobular nodularity, mosaic attenuation, and bronchial dilation. Agreement was similar for radiologists and pulmonologists. The prevalence on CT readings of most abnormalities (e.g. emphysema, bronchial wall thickening, mosaic attenuation, expiratory gas trapping) increased significantly with greater COPD severity, while the prevalence of centrilobular nodularity decreased. Concordances between visual scoring and quantitative scoring of emphysema, gas trapping and airway wall thickening were 75%, 87% and 65%, respectively. Conclusions Despite substantial inter-observer variation, visual assessment of chest CT scans in cigarette smokers provides information regarding lung disease severity; visual scoring may be complementary to quantitative evaluation.

Background COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of “unbiased” data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there has been no systematic assessment of the reproducibility of these subtypes. Objective We performed clustering analyses across ten cohorts in North America and Europe in order to assess the reproducibility of 1) correlation patterns of key COPD-related clinical characteristics and 2) clustering results. Methods We studied 17,146 individuals with COPD using identical methods and common COPD-related characteristics across cohorts (FEV1, FEV1/FVC, FVC, BMI, MMRC score, asthma, and cardiovascular comorbid disease). Correlation patterns between these clinical characteristics were assessed by principal components analysis (PCA). Cluster analysis was performed using k-medoids and hierarchical clustering, and concordance of clustering solutions was quantified with normalized mutual information (NMI), a metric that ranges from 0 to 1 with higher values indicating greater concordance. Results The reproducibility of COPD clustering subtypes across studies was modest (median NMI range 0.17 – 0.43). For methods that excluded individuals that did not clearly belong to any cluster, agreement was better but still suboptimal (median NMI range 0.32 – 0.60). Continuous representations of COPD clinical characteristics derived from PCA were much more consistent across studies. Conclusions Identical clustering analyses across multiple COPD cohorts showed modest reproducibility. COPD heterogeneity is better characterized by continuous disease traits coexisting in varying degrees within the same individual, rather than by mutually exclusive COPD subtypes.

Airway obstruction and parenchymal destruction underlie phenotype and severity in chronic obstructive pulmonary disease (COPD). We aimed to predict, by clinical and pulmonary function data, the predominant type and severity of pathological changes quantitatively assessed by computed tomography (CT).Airway wall thickness (AWT-Pi10) and percentage of lung area with X-ray attenuation values ,-950 HU (%LAA-950) were measured in 100 (learning set) out of 473 COPD outpatients undergoing clinical and functional evaluation. Original CT measurements were translated by principal component analysis onto a plane with the novel coordinates CT1 and CT2, depending on the difference (prevalent mechanism of airflow limitation) and on the sum (severity) of AWT-Pi10 and %LAA-950, respectively. CT1 and CT2, estimated in the learning set by cross-validated models of clinical and functional variables, were used to classify 373 patients in the testing set.A model based on diffusing capacity of the lung for carbon monoxide, total lung capacity and purulent sputum predicted CT1 (r50.64; p,0.01). A model based on forced expiratory volume in 1 s/vital capacity, functional residual capacity and purulent sputum predicted CT2 (r50.77; p,0.01). Classification of patients in the testing set obtained by model-predicted CT1 and CT2 reflected, according to correlations with clinical and functional variables, both COPD phenotype and severity.Multivariate models based on pulmonary function variables and sputum purulence classify patients according to overall severity and predominant phenotype of COPD as assessed quantitatively by CT. @ERSpublications Pulmonary function and sputum purulence models classify COPD patients by severity and phenotype as quantified by CT

BackgroundIn addition to lung involvement, several other diseases and syndromes coexist in patients with chronic obstructive pulmonary disease (COPD). Our purpose was to investigate the prevalence of idiopathic arterial hypertension (IAH), ischemic heart disease, heart failure, peripheral vascular disease (PVD), diabetes, osteoporosis, and anxious depressive syndrome in a clinical setting of COPD outpatients whose phenotypes (predominant airway disease and predominant emphysema) and severity (mild and severe diseases) were determined by clinical and functional parameters.MethodsA total of 412 outpatients with COPD were assigned either a predominant airway disease or a predominant emphysema phenotype of mild or severe degree according to predictive models based on pulmonary functions (forced expiratory volume in 1 second/vital capacity; total lung capacity %; functional residual capacity %; and diffusing capacity of lung for carbon monoxide %) and sputum characteristics. Comorbidities were assessed by objective medical records.ResultsEighty-four percent of patients suffered from at least one comorbidity and 75% from at least one cardiovascular comorbidity, with IAH and PVD being the most prevalent ones (62% and 28%, respectively). IAH prevailed significantly in predominant airway disease, osteoporosis prevailed significantly in predominant emphysema, and ischemic heart disease and PVD prevailed in mild COPD. All cardiovascular comorbidities prevailed significantly in predominant airway phenotype of COPD and mild COPD severity.ConclusionSpecific comorbidities prevail in different phenotypes of COPD; this fact may be relevant to identify patients at risk for specific, phenotype-related comorbidities. The highest prevalence of comorbidities in patients with mild disease indicates that these patients should be investigated for coexisting diseases or syndromes even in the less severe, pauci-symptomatic stages of COPD. The simple method employed to phenotype and score COPD allows these results to be translated easily into daily clinical practice.

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and represents one of the major causes of chronic morbidity. Cigarette smoking is the most important risk factor for COPD. In these patients, the airflow limitation is caused by a mixture of small airways disease and parenchyma destruction, the relative contribution of which varies from person to person. The twofold nature of the pathology has been studied in the past and according to some authors each patient should be classified as presenting a predominantly bronchial or emphysematous phenotype. In this study we applied various explorative analysis techniques (PCA, MCA, MDS) and recent unsupervised clustering methods (KHM) to study a large dataset, acquired from 415 COPD patients, to assess the presence of hidden structures in data corresponding to the different COPD phenotypes observed in clinical practice. In order to validate our methods, we compared the results obtained from a training set of 415 patients with lung density data acquired in a test set of 93 patients who underwent HRCT (High Resolution Computerized Tomography).