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BackgroundCOPD increases susceptibility to sleep disturbances, which may in turn predispose to increased respiratory symptoms. The objective of this study was to evaluate, in a population-based sample, the relationship between subjective sleep quality and risk of COPD exacerbations.MethodsData were obtained from the Canadian Cohort Obstructive Lung Disease (CanCOLD) study. Participants with COPD who had completed 18 months of follow-up were included. Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) and a three-factor analysis. Symptom-based (dyspnea or sputum change ≥ 48 h) and event-based (symptoms plus medication or unscheduled health services use) exacerbations were assessed. Association of PSQI with exacerbation rate was assessed by using negative binomial regression. Exacerbation-free survival was also assessed.ResultsA total of 480 participants with COPD were studied, including 185 with one or more exacerbations during follow-up and 203 with poor baseline sleep quality (PSQI score > 5). Participants with subsequent symptom-based exacerbations had higher median baseline PSQI scores than those without (6.0 [interquartile range, 3.0-8.0] vs 5.0 [interquartile range, 2.0-7.0]; P = .01), and they were more likely to have baseline PSQI scores > 5 (50.3% vs 37.3%; P = .01). Higher PSQI scores were associated with increased symptom-based exacerbation risk (adjusted rate ratio, 1.09; 95% CI, 1.01-1.18; P = .02) and event-based exacerbation risk (adjusted rate ratio, 1.10; 95% CI, 1.00-1.21; P = .048). The association occurred mainly in those with undiagnosed COPD. Strongest associations were with Factor 3 (sleep disturbances and daytime dysfunction). Time to symptom-based exacerbation was shorter in participants with poor sleep quality (adjusted hazard ratio, 1.49; 95% CI, 1.09-2.03).ConclusionsHigher baseline PSQI scores were associated with increased risk of COPD exacerbation over 18 months' prospective follow-up.

CT scan imaging provides high-resolution images of the lungs in patients with chronic respiratory diseases. Extensive research over the last several decades has focused on developing novel quantitative CT scan airway measurements that reflect abnormal airway structure. Despite many observational studies demonstrating that associations between CT scan airway measurements and clinically important outcomes such as morbidity, mortality, and lung function decline, few quantitative CT scan measurements are applied in clinical practice. This article provides an overview of the relevant methodologic considerations for implementing quantitative CT scan airway analyses and provides a review of the scientific literature involving quantitative CT scan airway measurements used in clinical or randomized trials and observational studies of humans. We also discuss emerging evidence for the clinical usefulness of quantitative CT scan imaging of the airways and discuss what is required to bridge the gap between research and clinical application. CT scan airway measurements continue to improve our understanding of disease pathophysiologic features, diagnosis, and outcomes. However, a literature review revealed a need for studies evaluating clinical benefit when quantitative CT scan imaging is applied in the clinical setting. Technical standards for quantitative CT scan imaging of the airways and high-quality evidence of clinical benefit from management guided by quantitative CT scan imaging of the airways are required. [ABSTRACT FROM AUTHOR]

Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions. Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning? Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n = 95), and established quantitative CT scan (n = 8), as well as demographic (n = 5) and spirometry (n = 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models. Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42% female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P <.05) or CT imaging features and spirometry to demographics (AUC, 0.877; P <.05) significantly improved the performance for predicting progression to COPD. Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD. [ABSTRACT FROM AUTHOR]

Texture-based radiomics analysis of lung computed tomography (CT) images has been shown to predict chronic obstructive pulmonary disease (COPD) status using machine learning models. However, various approaches are used and it is unclear which provides the best performance. To compare the most commonly used feature selection and classification methods and determine the optimal models for classifying COPD status in a mild, population-based COPD cohort. CT images from the multi-center Canadian Cohort Obstructive Lung Disease (CanCOLD) study were pre-processed by resampling the image to a 1mm isotropic voxel volume, segmenting the lung and removing the airways (VIDA Diagnostics Inc.), and applying a threshold of -1000HU-to-0HU. A total of 95 texture features were then extracted from each CT image. Combinations of 17 feature selection methods and 9 classifiers were tested and evaluated. In addition, the role of data cleaning (outlier removal and highly correlated feature removal) was evaluated. The area under the curve (AUC) from the receiver operating characteristic curve was used to evaluate model performance. A total of 1204 participants were evaluated (n = 602 no COPD, n = 602 COPD). There were no significant differences between the groups for female sex (no COPD = 46.3%; COPD = 38.5%; p = 0.77), or body mass index (no COPD = 27.7 kg/m2; COPD = 27.4 kg/m2; p = 0.21). The highest AUC value for predicting COPD status (AUC = 0.78 [0.73, 0.84]) was obtained following data cleaning and feature selection using Elastic Net with the Linear-SVM classifier. In a population-based cohort, the optimal combination for radiomics-based prediction of COPD status was Elastic Net as the feature selection method and Linear-SVM as the classifier. [ABSTRACT FROM AUTHOR]

Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an important goal for improving patient management. Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov , NCT00920348). Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measurements were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utilization. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% ± 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% ± 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% ± 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD. [ABSTRACT FROM AUTHOR]

In people with COPD, pulmonary gas-exchange efficiency may be impaired because of abnormal alveolar ventilation (V˙A), capillary perfusion (Q˙c), or both. Both have been reported in early and mild stages of the disease. Such derangements often accompany significant clinical consequences such as activity-related dyspnea and exercise intolerance. Although much attention has been paid to pharmacologic treatment of mechanical abnormalities in COPD (eg, bronchodilators to deflate the lungs), increasing neurochemical afferent activity, secondary to gas-exchange inefficiency, has remained elusive as a therapeutic target. Hence, in this invited review, we first summarize how dyspnea, leading to poor exercise tolerance in COPD, may be explained by an increased venous admixture resulting from low V˙A/Q˙c, or wasted ventilation related to high V˙A/Q˙c, or both. We review the conflicting evidence supporting current treatments for gas-exchange inefficiency and exercise tolerance that act primarily on V˙A (bronchodilators, antiinflammatory medications) or Q˙c (oral and inhaled vasodilators, almitrine, and supplemental oxygen). Finally, to address the current knowledge and health care gaps, we propose two independent clinical research foci that may lead to a better understanding of the role of pulmonary gas-exchange inefficiency and activity-related dyspnea in COPD: (1) enhanced and deeper phenotyping of patients with COPD with V˙A/Q˙c abnormalities and (2) evaluation of existing and novel pharmacologic treatments to improve gas-exchange inefficiency, exertional dyspnea, and exercise tolerance across the spectrum of COPD severity. [ABSTRACT FROM AUTHOR]

Purpose: Multiple commercial, open‐source, and academic software tools exist for objective quantification of lung density in computed tomography (CT) images. The purpose of this study was to evaluate the intersoftware reproducibility of CT lung density measurements. Methods: Computed tomography images from 50 participants from the COPDGeneTM cohort study were randomly selected for analysis; n = 10 participants across each global initiative for chronic obstructive lung disease (GOLD) grade (GOLD 0–IV). Academic‐based groups (n = 4) and commercial vendors (n = 4) participated anonymously to generate CT lung density measurements using their software tools. Computed tomography total lung volume (TLV), percentage of the low attenuation areas in the lung with Hounsfield unit (HU) values below −950HU (LAA950), and the HU value corresponding to the 15th percentile on the parenchymal density histogram (Perc15) were included in the analysis. The intersoftware bias and reproducibility coefficient (RDC) was generated with and without quality assurance (QA) for manual correction of the lung segmentation; intrasoftware bias and RDC was also generated by repeated measurements on the same images. Results: Intersoftware mean bias was within ±0.22 mL, ±0.46%, and ±0.97 HU for TLV, LAA950 and Perc15, respectively. The RDC was 0.35 L, 1.2% and 1.8 HU for TLV, LAA950 and Perc15, respectively. Intersoftware RDC remained unchanged following QA: 0.35 L, 1.2% and 1.8 HU for TLV, LAA950 and Perc15, respectively. All software investigated had an intrasoftware RDC of 0. The RDC was comparable for TLV, LAA950 and Perc15 measurements, respectively, for academic‐based groups/commercial vendor‐based software tools: 0.39 L/0.32 L, 1.2%/1.2%, and 1.7 HU/1.6 HU. Multivariable regression analysis showed that academic‐based software tools had greater within‐subject standard deviation of TLV than commercial vendors, but no significant differences between academic and commercial groups were found for LAA950 or Perc15 measurements. Conclusions: Computed tomography total lung volume and lung density measurement bias and reproducibility was reported across eight different software tools. Bias was negligible across vendors, reproducibility was comparable for software tools generated by academic‐based groups and commercial vendors, and segmentation QA had negligible impact on measurement variability between software tools. In summary, results from this study report the amount of additional measurement variability that should be accounted for when using different software tools to measure lung density longitudinally with well‐standardized image acquisition protocols. However, intrasoftware reproducibility was deterministic for all cases so use of the same software tool to reduce variability for serial studies is highly recommended. [ABSTRACT FROM AUTHOR]

Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation, caused by emphysema and small airways disease (SAD). Computed tomography (CT) coupled with image analysis enables the quantification of these abnormalities; however, the optimum method for doing so has not been determined.Objectives: This study aims to compare two CT quantitative analysis techniques, disease probability measure (DPM) and parametric response mapping (PRM), and assess their relationship with specific physiological measures of SAD.Methods: Subjects with mild to moderate COPD, never smokers, and healthy ex-smokers were recruited. Each had airway oscillometry and multiple-breath nitrogen washout, measuring peripheral airway resistance, peripheral airway reactance, and acinar airway inhomogeneity. Subjects also had an inspiratory and expiratory chest CT, with DPM and PRM analysis performed by coregistering images and classifying each voxel as normal, emphysema, or nonemphysematous gas trapping related to SAD.Results: Thirty-eight subjects with COPD, 18 never smokers, and 23 healthy ex-smokers were recruited. There were strong associations between DPM and PRM analysis when measuring gas trapping (ρ = 0.87; P < 0.001) and emphysema (ρ = 0.99; P < 0.001). DPM assigned significantly more voxels as emphysema and gas trapped than PRM (P < 0.001). Both techniques showed significantly greater emphysema and gas trapping in subjects with COPD than in never smokers and ex-smokers (P < 0.001). All CT measures had significant associations with peripheral airway resistance and reactance, with disease probability measure of nonemphysematous gas trapping related to SAD having the strongest independent association with peripheral airway resistance (β = 0.42; P = 0.001) and peripheral airway reactance (β = 0.41; P = 0.001). Emphysema measures had the strongest associations with acinar airway inhomogeneity (β = 0.35-0.38).Conclusions: These results provide further validation for the use of DPM/PRM analysis in COPD by demonstrating significant relationships with specific physiological measures of SAD. [ABSTRACT FROM AUTHOR]

Lung diseases are increasing in prevalence and overall burden worldwide. To stem the tide, more and more national and international guidelines are recommending the use of various diagnostic algorithms that are disease specific. There is growing consensus among the respiratory community that although patient histories and lung function testing are the minimum required for clinical examinations, these tests alone are not sufficient for disease characterization. Therefore, the use of computed tomography (CT) imaging is increasing used in clinical decision making for lung diseases. Lung diseases affect various components of lung, including the small airways, lung parenchyma, the interstitial space and the pulmonary vasculature. Quantitative CT (QCT) methods are emerging and are increasingly available using commercial software to quantify the underlying disease components, and a growing body of evidence suggests that QCT is an important tool in the clinical setting to help accurately and reproducibly detect where the disease is located in the lung, and to quantify the extent and overall severity for several lung diseases. Furthermore, this growing body of evidence has promoted the use of thoracic QCT to the point that it is now considered by many as an indispensable technology for longitudinal analysis and intervention trials. Many QCT imaging measurements are available to the respiratory physician, and the aim of this review is to introduce and describe pulmonary QCT imaging measurements and methodologies. [ABSTRACT FROM AUTHOR]

While the recent development of quantitative imaging methods have led to their increased use in the diagnosis and management of many chronic diseases, medical imaging still plays a limited role in the management of chronic obstructive pulmonary disease (COPD). In this review we highlight three pulmonary imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI) and optical coherence tomography (OCT) imaging and the COPD biomarkers that may be helpful for managing COPD patients. We discussed the current role imaging plays in COPD management as well as the potential role quantitative imaging will play by identifying imaging phenotypes to enable more effective COPD management and improved outcomes. [ABSTRACT FROM AUTHOR]

Pulmonary ventilation may be visualized and measured using hyperpolarized3He magnetic resonance imaging (MRI) while emphysema and its distribution can be quantified using thoracic computed tomography (CT). Our objective was to phenotype ex-smokers with COPD based on the apical-to-basal distribution of ventilation abnormalities and emphysema to better understand how these phenotypes change regionally as COPD progresses. We evaluated 100 COPD ex-smokers who provided written informed consent and underwent spirometry, CT and3He MRI.3He MRI ventilation imaging was used to quantify the ventilation defect percent (VDP) for whole-lung and individual lung lobes. Regional VDP was used to generate the apical-lung (AL)-to-basal-lung (BL) difference (ΔVDP); a positive ΔVDP indicated AL-predominant and negative ΔVDP indicated BL-predominant ventilation defects. Emphysema was quantified using the relative-area-of-the-lung ≤−950HU (RA950) of the CT density histogram for whole-lung and individual lung lobes. The AL-to-BL RA950difference (ΔRA950) was generated with a positive ΔRA950indicating AL-predominant emphysema and a negative ΔRA950indicating BL-predominant emphysema. Seventy-two ex-smokers reported BL-predominant MRI ventilation defects and 71 reported AL-predominant CT emphysema. BL-predominant ventilation defects (AL/BL: GOLD I = 18%/82%, GOLD II = 24%/76%) and AL-predominant emphysema (AL/BL: GOLD I = 84%/16%, GOLD II = 72%/28%) were the major phenotypes in mild-moderate COPD. In severe COPD there was a more uniform distribution for ventilation defects (AL/BL: GOLD III = 40%/60%, GOLD IV = 43%/57%) and emphysema (AL/BL: GOLD III = 64%/36%, GOLD IV = 43%/57%). Basal-lung ventilation defects predominated in mild-moderate GOLD grades, and a more homogeneous distribution of ventilation defects was observed in more advanced grade COPD; these differences suggest that over time, regional ventilation abnormalities become more homogenously distributed during disease progression. [ABSTRACT FROM PUBLISHER]

Pulmonary vascular disease is a common complication of chronic obstructive pulmonary disease (COPD), and an important risk factor for COPD exacerbations and death. We explored the relationship between pulmonary artery volumes measured using thoracic computed tomography (CT) and lung structure-function measured using spirometry, CT and magnetic resonance imaging (MRI) in 124 ex-smokers with (n = 68) and without (n = 56) airflow obstruction, and a control group of 35 never-smokers. We observed significantly greater main (p =.01), right (p =.001) and total (p =.003) pulmonary artery volumes in ex-smokers with airflow obstruction as compared to ex-smokers without airflow obstruction. There were also significantly greater pulmonary artery volumes in both ex-smoker subgroups, compared to the never-smoker subgroup (p =.008). For all participants, there were significant correlations for pulmonary artery volumes with the ratio of the forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC), the diffusing capacity of the lung for carbon monoxide (DLCO%pred), airway count, MRI ventilation defect percent and MRI apparent diffusion coefficients. In ex-smokers, ventilation defect percent was significantly correlated with right (r = 0.27, p =.02) and total (r = 0.25, p =.03) pulmonary artery volumes. Multivariate zero-inflated Poisson regression analysis showed that FEV1%pred (p =.004), DLCO%pred (p =.03), the six minute walk distance (p =.04) and total pulmonary artery volume (p =.03) were significant predictors of acute exacerbations of COPD, while the number of previous exacerbations was not. In conclusion, pulmonary artery enlargement measured using thoracic CT was observed even in ex-smokers without airflow obstruction and was predictive of COPD exacerbations in ex-smokers with airflow obstruction. [ABSTRACT FROM AUTHOR]

It is well-established that COPD patients have a burden of vascular disease that cannot be fully-explained by smoking history but the mechanistic links between atherosclerosis and pulmonary disease in COPD patients are not well-understood. Moreover, in ex-smokers without symptoms or other evidence of COPD, subclinical pulmonary and vascular disease, although potentially present, has not been described or evaluated. Hence our aim was to use sensitive three-dimensional (3D) pulmonary and carotid imaging to quantify pulmonary airway/parenchyma abnormalities and atherosclerosis in ex-smokers without airflow limitation or symptoms consistent with COPD. We evaluated 61 subjects without airflow limitation including 34 never- (72 ± 6 years) and 27 ex-smokers (73 ± 9 years), who provided written informed consent to spirometry, plethysmography, 3He magnetic resonance imaging (MRI) and carotid ultrasound (US) and, for ex-smokers alone, thoracic X-ray computed tomography (CT). Ex-smokers had significantly greater 3He ventilation defect percent (VDP = 7%, p = 0.001) and carotid total plaque volume (TPV = 250 mm3, p = 0.002) than never-smokers, although there were no significant differences for spirometry or plethysmography, and CT airway and emphysema measurements were normal. There were univariate relationships for 3He VDP with carotid intima media thickness (IMT, r = 0.42, p = 0.004), TPV (r = 0.41, p = 0.006) and vessel wall volume (VWV, r = 0.40, p = 0.007). Multivariate models that included age, BMI, FEV1, DLCO and VDP showed that only VDP significantly predicted IMT (β = 0.41, p = 0.001), VWV (β = 0.45, p = 0.003) and TPV (β = 0.38, p = 0.005). In summary, there was imaging evidence of mild airways disease and carotid plaque burden that were related and significantly greater in ex-smokers without airflow limitation than in never-smokers. [ABSTRACT FROM AUTHOR]

Noble gas pulmonary magnetic resonance imaging ( MRI) is transitioning away from 3He to 129Xe gas, but the physiological/clinical relevance of 129Xe apparent diffusion coefficient ( ADC) parenchyma measurements is not well understood. Therefore, our objective was to generate 129Xe MRI ADC for comparison with 3He ADC and with well-established measurements of alveolar structure and function in older never-smokers and ex-smokers with chronic obstructive pulmonary disease ( COPD). In four never-smokers and 10 COPD ex-smokers, 3He ( b = 1.6 sec/cm2) and 129Xe ( b = 12, 20, and 30 sec/cm2) ADC, computed tomography ( CT) density-threshold measurements, and the diffusing capacity for carbon monoxide (DLCO) were measured. To understand regional differences, the anterior-posterior ( APG) and superior-inferior (∆ SI) ADC differences were evaluated. Compared to never-smokers, COPD ex-smokers showed greater 3He ADC ( P = 0.006), 129Xe ADCb12 ( P = 0.006), and ADCb20 ( P = 0.006), but not for ADCb30 ( P > 0.05). Never-smokers and COPD ex-smokers had significantly different APG for 3He ADC ( P = 0.02), 129Xe ADCb12 ( P = 0.006), and ADCb20 ( P = 0.01), but not for ADCb30 ( P > 0.05). ∆ SI for never- and ex-smokers was significantly different for 3He ADC ( P = 0.046), but not for 129Xe ADC ( P > 0.05). There were strong correlations for DLCO with 3He ADC and 129Xe ADCb12 (both r = −0.95, P < 0.05); in a multivariate model 129Xe ADCb12 was the only significant predictor of DLCO ( P = 0.049). For COPD ex-smokers, CT relative area <−950 HU ( RA950) correlated with 3He ADC ( r = 0.90, P = 0.008) and 129Xe ADCb12 ( r = 0.85, P = 0.03). In conclusion, while 129Xe ADCb30 may be appropriate for evaluating subclinical or mild emphysema, in this small group of never-smokers and ex-smokers with moderate-to-severe emphysema, 129Xe ADCb12 provided a physiologically appropriate estimate of gas exchange abnormalities and alveolar microstructure. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To improve intra- and interobserver variability and enable the use of functional magnetic resonance imaging (MRI) for multicenter, multiobserver studies, we generated a semiautomated segmentation method for hyperpolarized helium-3 (3He) MRI. Therefore the objective of this study was to compare the reproducibility and spatial agreement of manual and semiautomated segmentation of 3He MRI ventilation defect volume (VDV) and ventilation volume (VV) in subjects with asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF). Materials and Methods: The multistep semiautomated segmentation method we developed employed hierarchical K-means clustering to classify 3He MRI pixel intensity values into five user-determined clusters ranging from signal void to hyperintense. A seeded region-growing algorithm was also used to segment the 1H MRI thoracic cavity for coregistration to the 3He cluster-map, generating VDV and VV. Results: We compared manual segmentation performed by an expert observer and semiautomated measurements of 3He MRI VDV and observed strong significant correlations between the volumes generated using each method (asthma, n = 5, r = 0.89, P < .0001; COPD, n = 5, r = 0.84, P < .0001; CF, n = 5, r = 0.89, P < .0001). Semiautomated VDV had high interobserver reproducibility (coefficient of variation [CV] = 7%, intraclass correlation coefficient [ICC] = 0.96); intraobserver reproducibility was significantly higher for semiautomated (CV = 5%, ICC = 1.00) compared to manual VDV (CV = 12%, ICC = 0.98). Spatial agreement for VV determined using the Dice coefficient (D) was also high for all disease states (asthma, D = 0.95; COPD, D = 0.88; CF, D = 0.90). Conclusions: Semiautomated segmentation 3He MRI provides excellent inter- and intraobserver precision with high spatial and quantitative agreement with manual measurements enabling its use in longitudinal studies. [ABSTRACT FROM AUTHOR]

An introduction is presents in which the editor discusses various reports within the issue including the chronic obstructive pulmonary disease diagnosis,its symptoms and treatment.