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INTRODUCTION:As there is an increasing number of multicentre lung imaging studies with MRI in patients, dedicated reference phantoms are required to allow for the assessment and comparison of image quality in multi-vendor and multi-centre environments. However, appropriate phantoms for this purpose are so far not available commercially. It was therefore the purpose of this project to design and apply a cost-effective and simple to use reference phantom which addresses the specific requirements for imaging the lungs with MRI. METHODS:The phantom was designed to simulate 4 compartments (lung, blood, muscle and fat) which reflect the specific conditions in proton-MRI of the chest. Multiple phantom instances were produced and measured at 15 sites using a contemporary proton-MRI protocol designed for an in vivo COPD study at intervals over the course of the study. Measures of signal- and contrast-to-noise ratio, as well as structure and edge depiction were extracted from conventionally acquired images using software written for this purpose. RESULTS:For the signal to noise ratio, low intra-scanner variability was found with 4.5% in the lung compartment, 4.0% for blood, 3.3% for muscle and 3.7% for fat. The inter-scanner variability was substantially higher, with 41%, 32%, 27% and 32% for the same order of compartments. In addition, measures of structure and edge depiction were found to both vary significantly among several scanner types and among scanners of the same model which were equipped with different gradient systems. CONCLUSION:The described reference phantom reproducibly quantified image quality aspects and detected substantial inter-scanner variability in a typical pulmonary multicentre proton MRI study, while variability was greater in lung tissue compared to other tissue types. Accordingly, appropriate reference phantoms can help to detect bias in multicentre in vivo study results and could also be used to harmonize equipment or data.

Objective In large cohort studies comorbidities are usually self- reported by the patients. This way to collect health information only represents conditions known, memorized and openly reported by the patients. Several studies addressed the relationship between self- reported comorbidities and medical records or pharmacy data, but none of them provided a structured, documented method of evaluation. We thus developed a detailed procedure to compare self- reported comorbidities with information on comorbidities derived from medication inspection. This was applied to the data of the German COPD cohort COSYCONET. Methods Approach I was based solely on ICD10-Codes for the diseases and the indications of medications. To overcome the limitations due to potential non-specificity of medications, Approach II was developed using more detailed information, such as ATC-Codes specific for one disease. The relationship between reported comorbidities and medication was expressed by a four-level concordance score. Results Approaches I and II demonstrated that the patterns of concordance scores markedly differed between comorbidities in the COSYCONET data. On average, Approach I resulted in more than 50% concordance of all reported diseases to at least one medication. The more specific Approach II showed larger differences in the matching with medications, due to large differences in the disease-specificity of drugs. The highest concordance was achieved for diabetes and three combined cardiovascular disorders, while it was substantial for dyslipidemia and hyperuricemia, and low for asthma. Conclusion Both approaches represent feasible strategies to confirm self-reported diagnoses via medication. Approach I covers a broad spectrum of diseases and medications but is limited regarding disease-specificity. Approach II uses the information from medications specific for a single disease and therefore can reach higher concordance scores. The strategies described in a detailed and reproducible manner are generally applicable in large studies and might be useful to extract as much information as possible from the available data.

The authors would like to amend this article based on the discovery of a number of errors that came to light after publication. We recognized a number of errors that made it necessary to repeat a part of the experiments. Please view the corrected text, table and figures here.

Although hyperlipidemia is common in COPD, its relationship to comorbidities, risk factors and lung function in COPD has not been studied in detail. Using the baseline data of the COSYCONET cohort we addressed this question. Data from 1746 COPD patients (GOLD stage 1-4; mean age 64.6 y, mean FEV1%pred 57%) were evaluated, focusing on the comorbidities hyperlipidemia, diabetes and cardiovascular complex (CVC; including arterial hypertension, cardiac failure, ischemic heart disease). Risk factors comprised age, gender, BMI, and packyears of smoking. The results of linear and logistic regression analyses were implemented into a path analysis model describing the multiple relationships between parameters. Hyperlipidemia (prevalence 42.9%) was associated with lower intrathoracic gas volume (ITGV) and higher forced expiratory volume in 1 second (FEV1) when adjusting for its multiple relationships to risk factors and other comorbidities. These findings were robust in various statistical analyses. The associations between comorbidities and risk factors were in accordance with previous findings, thereby underlining the validity of our data. In conclusion, hyperlipidemia was associated with less hyperinflation and airway obstruction in patients with COPD. This surprising result might be due to different COPD phenotypes in these patients or potential anti-inflammatory effects of hyperlipidemia-related medication.

Introduction: Analysis of exhaled breath condensate (EBC) is a noninvasive method to access the epithelial lining fluid of the lungs. Due to standardization problems the method has not entered clinical practice. The aim of the study was to assess the comparability for two commercially available devices in healthy controls. In addition, we assessed different breathing patterns in healthy controls with protein markers to analyze the source of the EBC. Methods: EBC was collected from ten subjects using the RTube and ECoScreen Turbo in a randomized crossover design, twice with every device - once in tidal breathing and once in hyperventilation. EBC conductivity, pH, surfactant protein A, Clara cell secretory protein and total protein were assessed. Bland-Altman plots were constructed to display the influence of different devices or breathing patterns and the intra-class correlation coefficient (ICC) was calculated. The volatile organic compound profile was measured using the electronic nose Cyranose 320. For the analysis of these data, the linear discriminant analysis, the Mahalanobis distances and the cross-validation values (CVV) were calculated. Results: Neither the device nor the breathing pattern significantly altered EBC pH or conductivity. ICCs ranged from 0.61 to 0.92 demonstrating moderate to very good agreement. Protein measurements were greatly influenced by breathing pattern, the device used, and the way in which the results were reported. The electronic nose could distinguish between different breathing patterns and devices, resulting in Mahalanobis distances greater than 2 and CVVs ranging from 64% to 87%. Conclusion: EBC pH and (to a lesser extent) EBC conductivity are stable parameters that are not influenced by either the device or the breathing patterns. Protein measurements remain uncertain due to problems of standardization. We conclude that the influence of the breathing maneuver translates into the necessity to keep the volume of ventilated air constant in further studies.