Matteo Paoletti

Identification of a predominant COPD phenotype in clinical practice

BACKGROUND Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation caused by small airways increased resistance and/or terminal airspaces emphysematous destruction. Spirometric detection of not fully reversible airflow limitation unifies under the acronym COPD, a spectrum of heterogeneous conditions, whose clinical presentations may be substantially different. In a cross-sectional study we aimed to ascertain whether COPD phenotypes reflecting different mechanisms of airflow limitation could be clinically identified. METHODS Multidimensional scaling was used to visualize as a single point in a two-dimension space the multidimensional variables derived from each of 322 COPD patients (derivation set) by clinical, functional, and chest radiographic evaluation. Cluster analysis assigned then a cluster membership to each patient data point. Finally, using cluster membership as dependent variable and all data acquired as independent variables, we developed multivariate models to prospectively classify another group of 93 COPD patients (validation set) in whom high-resolution computerized tomography (HRCT) density parameters were measured. RESULTS A multivariate model based on nine variables acquired from the derivation set by history (sputum characteristics), physical examination (adventitious sounds, hyperresonance), FEV1/VC, and chest radiography (increased vascular markings, bronchial wall thickening, increased lung volume, reduced lung density) partitioned the validation set into two groups whose clinical, functional, chest radiographic, and HRCT characteristics corresponded to either an airways obstructive or a parenchymal destructive COPD phenotype. CONCLUSION Patients with COPD can be assigned a clinical phenotype reflecting the prevalent mechanism of airflow limitation. The standardized identification of the predominant phenotype may permit to clinically characterize COPD beyond its unifying spirometric definition.

Explorative data analysis techniques and unsupervised clustering methods to support clinical assessment of Chronic Obstructive Pulmonary Disease (COPD) phenotypes

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).

Pulmonary function and sputum characteristics predict computed tomography phenotype and severity of COPD.

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 (r = 0.64; p<0.01). A model based on forced expiratory volume in 1 s/vital capacity, functional residual capacity and purulent sputum predicted CT2 (r = 0.77; 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. Pulmonary function and sputum purulence models classify COPD patients by severity and phenotype as quantified by CT http://ow.ly/kyP6d

BODE-index, modified BODE-index and ADO-score in Chronic Obstructive Pulmonary Disease: Relationship with COPD phenotypes and CT lung density changes

Abstract COPD is a heterogeneous disorder whose assessment is going to be increasingly multidimensional. Grading systems such as BODE (Body-Mass Index, Obstruction, Dyspnea, Exercise), mBODE (BODE modified in grading of walked distance), ADO (Age, Dyspnea, Obstruction) are proposed to assess COPD severity and outcome. Computed tomography (CT) is deemed to reflect COPD lung pathologic changes. We studied the relationship of multidimensional grading systems (MGS) with clinically determined COPD phenotypes and CT lung density. Seventy-two patients underwent clinical and chest x-ray evaluation, pulmonary function tests (PFT), 6-minute walking test (6MWT) to derive: predominant COPD clinical phenotype, BODE, mBODE, ADO. Inspiratory and expiratory CT was performed to calculate mean lung attenuation (MLA), relative area with density below-950 HU at inspiration (RAI-950), and below -910 HU at expiration (RAE-910). MGS, PFT, and CT data were compared between bronchial versus emphysematous COPD phenotype. MGS were correlated with CT data. The prediction of CT density by means of MGS was investigated by direct and stepwise multivariate regression. MGS did not differ in clinically determined COPD phenotypes. BODE was more closely related and better predicted CT findings than mBODE and ADO; the better predictive model was obtained for CT expiratory data; stepwise regression models of CT data did not include 6MWT distance; the dyspnea score MRC was included only to predict RA-950 and RA-910 which quantify emphysema extent. BODE reflect COPD severity better than other MGS, but not its clinical heterogeneity. 6MWT does not significantly increase BODE predictivity of CT lung density changes.

Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts

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 10 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, body mass index, Modified Medical Research Council 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 normalised 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 characterised by continuous disease traits coexisting in varying degrees within the same individual, rather than by mutually exclusive COPD subtypes.

Chronic Obstructive Pulmonary Disease: Pulmonary Function and CT Lung Attenuation Do Not Show Linear Correlation

PURPOSE To determine whether the relationship between pulmonary function and computed tomographic (CT) lung attenuation in chronic obstructive pulmonary disease (COPD), which is traditionally described with single univariate and multivariate statistical models, could be more accurately described with a multiple model estimation approach. MATERIALS AND METHODS The study was approved by the local ethics committee. All participants provided written informed consent. The prediction of the percentage area with CT attenuation values less than -950 HU at inspiration (%LAA-950insp) and less than -910 HU at expiration (%LAA-910exp) obtained with single univariate and multivariate models was compared with that obtained with a multiple model estimation approach in 132 patients with COPD. RESULTS At univariate analysis, %LAA-950insp and %LAA-910exp values higher than the mean value of this cohort (19.1% and 22.0%) showed better correlation with percentage of predicted diffusing capacity of lung for carbon monoxide (Dlco%) than with airflow obstruction (forced expiratory volume in 1 second [FEV1]/vital capacity [VC]). Conversely, %LAA-950insp and %LAA-910exp values lower than the mean value were correlated with FEV1/VC but not with Dlco%. Multiple model estimation performed with two multivariate regressions, each selecting the most appropriate functional variables (FEV1/VC for mild parenchymal destruction, Dlco% and functional residual capacity for severe parenchymal destruction), predicted better than single multivariate regression both %LAA-950insp (R(2) = 0.75 vs 0.46) and %LAA-910exp (R(2) = 0.83 vs 0.63). CONCLUSION The relationship between pulmonary function data and CT densitometric changes in COPD varies with the level of lung attenuation impairment. The nonlinear profile of this relationship is accurately predicted with a multiple model estimation approach.

RETRACTED: Development and Validation of a New Outcome Score in Subglottic Stenosis

BACKGROUND We prospectively evaluated a clinical and endoscopic score, the tracheal endoscopic clinical score (TECS), developed as a disease-specified outcome measure in adult patients undergoing operation for subglottic stenosis. We also performed a retrospective chart review to identify preoperative and intraoperative risk factors for worse TECS. METHODS The TECS includes endoscopic (vocal cord and glottic function, anastomotic healing, and patency) and interview (respiration, voice, swallow) variables, and was administered at 6-month follow-up. Endoscopic and subjective domains were weighted to obtain a continuous TECS index ranging from 0 (best) to 1 (worse). The TECS and preoperative variables relationships were evaluated by univariate and multivariate analysis. RESULTS We collected data (January 2009 to December 2010) from 30 patients (mean age, 48.3±19 years) undergoing subglottic resection and primary reconstruction. Stenosis etiology was postintubation (n=8), idiopathic (n=2), tracheostomy (n=18), and malignant (n=2). Surgery included Pearson operation with (n=7) or without (n=23) a Liberman-Mathisen cricoplasty. Mean length of resected trachea was 30.5±13.5 mm, and mean hospital stay was 7.4 days. Mortality rate was 1 patient (3.3%). The univariate analysis showed positive correlation between 6-month TECS and degree of stenosis (McCaffrey and Cotton scale 0 to 4) stage 4, tracheostomy or T-tube at surgery, bottleneck-type transition stenosis, and resection length. At multivariate analysis, the presence of tracheostomy, bottleneck-type transition stenosis and resection length were indicators of worse postoperative functional result. CONCLUSIONS The TECS seems to be a valid and simple instrument to identify preoperative variables predicting worse results and to assess postoperative outcome. Validation on larger series is necessary.

Is intrathoracic tracheal collapsibility correlated to clinical phenotypes and sex in patients with COPD

A substantial proportion of patients with chronic obstructive pulmonary disease (COPD) develops various degree of intrathoracic tracheal collapsibility. We studied whether the magnitude of intrathoracic tracheal collapsibility could be different across clinical phenotypes and sex in COPD. Intrathoracic tracheal collapsibility measured at paired inspiratory–expiratory low dose computed tomography (CT) and its correlation with clinical, functional, and CT-densitometric data were investigated in 69 patients with COPD according to their predominant conductive airway or emphysema phenotypes and according to sex. Intrathoracic tracheal collapsibility was higher in patients with predominant conductive airway disease (n=28) and in females (n=27). Women with a predominant conductive airway phenotype (n=10) showed a significantly greater degree of collapsibility than women with predominant emphysema (28.9%±4% versus 11.6%±2%; P<0.001). Intrathoracic tracheal collapsibility was directly correlated with inspiratory–expiratory volume variation at CT and with forced expiratory volume (1 second), and inversely correlated with reduced CT lung density and functional residual capacity. Intrathoracic tracheal collapsibility was not correlated with cough and wheezing; however, intrathoracic tracheal collapsibility and clinical phenotypes of COPD are closely correlated. In patients with a predominant emphysematous phenotype, a reduced collapsibility may reflect the mechanical properties of the stiff hyperinflated emphysematous lung. The high collapsibility in patients with predominant airway disease, mild airway obstruction, and in women with this phenotype may reflect chronic airway inflammation. The lack of relationship with such symptoms as wheezing, cough, and dyspnea could indicate that intrathoracic tracheal collapsibility itself should be considered neither an abnormal feature of COPD nor a relevant clinical finding.

Emphysematous and Nonemphysematous Gas Trapping in Chronic Obstructive Pulmonary Disease: Quantitative CT Findings and Pulmonary Function

Purpose To identify a prevalent computed tomography (CT) subtype in patients with chronic obstructive pulmonary disease (COPD) by separating emphysematous from nonemphysematous contributions to total gas trapping and to attempt to predict and grade the emphysematous gas trapping by using clinical and functional data. Materials and Methods Two-hundred and two consecutive eligible patients (159 men and 43 women; mean age, 70 years [age range, 41-85 years]) were prospectively studied. Pulmonary function and CT data were acquired by pulmonologists and radiologists. Noncontrast agent-enhanced thoracic CT scans were acquired at full inspiration and expiration, and were quantitatively analyzed by using two software programs. CT parameters were set as follows: 120 kVp; 200 mAs; rotation time, 0.5 second; pitch, 1.1; section thickness, 0.75 mm; and reconstruction kernels, b31f and b70f. Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds (percentage area with CT attenuation values less than -950 HU at inspiration and percentage area with CT attenuation values less than -856 HU at expiration) was compared with that obtained by coregistration analysis. A logistic regression model on the basis of anthropometric and functional data was cross-validated and trained to classify patients with COPD according to the relative contribution of emphysema to total gas trapping, as assessed at CT. Results Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds was highly correlated (r = 0.99) with that obtained by coregistration analysis. Four groups of patients were distinguished according to the prevalent CT subtype: prevalent emphysematous gas trapping, prevalent functional gas trapping, mixed severe, and mixed mild. The predictive model included predicted forced expiratory volume in 1 second/vital capacity, percentage of predicted forced expiratory volume in 1 second, percentage of diffusing capacity for carbon monoxide, and body mass index as emphysema regressors at CT, with 81% overall accuracy in classifying patients according to its extent. Conclusion The relative contribution of emphysematous and nonemphysematous gas trapping obtained by coregistration of inspiratory and expiratory CT scanning can be determined accurately by difference of CT inspiratory and expiratory density thresholds. CT extent of emphysema can be predicted with accuracy suitable for clinical purposes by pulmonary function data and body mass index.