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BACKGROUND: Epidemiologic studies have shown that patients with severe asthma have increased risk of pulmonary embolism, in particular patients with frequent asthma exacerbations. Therefore, we hypothesized that asthma exacerbations are associated with increased hemostatic activity.OBJECTIVE: To investigate if induced loss of asthma control is associated with changes in coagulation and fibrinolytic parameters in peripheral blood.METHODS: We performed a prospective, inhaled steroid-withdrawal study in 23 patients with moderate to moderately severe asthma, consisting of a baseline visit and a visit after loss of asthma control. During the visits we measured asthma control questionnaire (ACQ), atopy, lung function, inflammatory markers (eosinophils and neutrophils), and hemostatic parameters in plasma.RESULTS: Complete cessation of inhaled corticosteroids led to a loss of asthma control in 22 out of 23 patients. We found increased asthma symptoms (ACQ 0.9 vs. 2.9, pCONCLUSION: Loss of asthma control after cessation of inhaled corticosteroids does not lead to increased hemostatic activation in patients with moderate to moderately severe asthma. This suggests that more severe inflammation or additional risk factors are required for activation of coagulation or reduction of fibrinolysis in asthma. This article is protected by copyright. All rights reserved.

Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whether Aspergillus fumigatus colonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined the A. fumigatus colonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. The P value was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized by A. fumigatus . In total, 3 subjects were misclassified, resulting in a cross-validated accuracy of the Cyranose detecting IA of 89% ( P = 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate that A. fumigatus colonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.

Pseudomonas aeruginosa (PA) is one of the most pathogenic bacteria infecting the airways of patients with cystic fibrosis (CF). Sputum culture is the gold standard for detecting PA. However, this method is time-consuming. In this study we hypothesized that volatile organic compounds (VOCs) in exhaled breath can discriminate between PA infected and non-infected CF patients. Design: Cross-sectional baseline analysis of longitudinal cohort study. We collected exhaled air of both pediatric and adult CF patients with and without PA airways infection, in which VOCs were identified using Gas Chromatography – Mass Spectrometry (GC-MS). Infection was defined as culturing PA on the day of breath sampling, or chronic colonization with PA (in >50% of cultures (minimum of 4 samples) over the previous 12 months, or chronic maintenance treatment for PA). To identify VOCs relevant for distinction between the groups, univariate analysis (Mann Withney U test) was applied for each GC-MS compound. Features with a p-value below 0.05 retained for linear discriminant analysis. Diagnostic performed was evaluated by calculation of the area under the receiver operating characteristic - curve including 95% confidence Interval (CI). Complete data was available of 18 pediatric and 22 adult CF patients; 20 with and 20 without PA infection. Exploratory analysis of unselected patients showed that 3 out of 138 VOCs were consistently different in PA infected and non-infected patients, resulting in an AUC of 0.75 (CI 0.59 – 0.92). We discriminated PA infected and non-infected CF patients based on 3 VOCs with moderate accuracy. However, these preliminary results need to be validated in an independent patient cohort.

Breath tests cover the fraction of nitric oxide in expired gas (FENO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for FENO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and FENO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.

SummaryBackground Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids (ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control. Objectives To determine whether exhaled volatile organic compounds (VOCs) as measured by gas-chromatography/mass-spectrometry (GC/MS) and electronic nose (eNose) technology discriminate between clinically stable and unstable episodes of asthma. Methods Twenty-three patients with (partly) controlled mild to moderate persistent asthma using ICS were included in this prospective steroid withdrawal study. Exhaled metabolites were measured at baseline, during loss of control and after recovery. Standardized sampling of exhaled air was performed, after which samples were analysed by GC/MS and eNose. Univariate analysis of covariance (ANCOVA), followed by multivariate principal component analysis (PCA) was used to reduce data dimensionality. Next paired t tests were utilized to analyse within-subject breath profile differences at the different time-points. Finally, associations between exhaled metabolites and sputum inflammation markers were examined. Results Breath profiles by eNose showed 95% (21/22) correct classification for baseline vs loss of control and 86% (19/22) for loss of control vs recovery. Breath profiles using GC/MS showed accuracies of 68% (14/22) and 77% (17/22) for baseline vs loss of control and loss of control vs recovery, respectively. Significant associations between exhaled metabolites captured by GC/MS and sputum eosinophils were found (Pearson r≥.46, P

Exhaled air contains many volatile organic compounds (VOCs) that are the result of normal and disease-associated metabolic processes anywhere in the body. Different omics techniques can assess the pattern of these VOCs. One such omics technique suitable for breath analysis is represented by electronic noses (eNoses), providing fingerprints of the exhaled VOCs, called breathprints. Breathprints have been shown to be altered in different disease states, including in asthma and COPD. This review describes the current status on clinical validation and application of breath analysis by electronic noses in the diagnosis and monitoring of chronic airways diseases. Furthermore, important methodological issues including breath sampling, modulating factors and incompatibility between eNoses are raised and discussed. Next steps towards clinical application of electronic noses are provided, including further validation in suspected disease, assessment of the influence of different comorbidities, the value in longitudinal monitoring of patients with asthma and COPD and the possibility to predict treatment responses. Eventually, a Breath Cloud may be constructed, a large database containing disease-specific breathprints. When collaborative efforts are put into optimization of this technique, it can provide a rapid and non-invasive first line diagnostic test.

Introduction: Exacerbations/loss of control (LOC) represent the major burden in asthma care and management. These events are clinically manifested by changes in symptoms and lung function, which are associated with changes in biomarkers related to airway pathophysiology. Direct assessment of exhaled air volatile organic compounds (VOCs) could capture exacerbation/LOC by changed metabolomic fingerprints and this could provide a Point Of Care diagnostic in poor perceivers of exacerbation symptoms. Aims: To identify critical VOC patterns associated with development and recovery of LOC after: a) withdrawal and b) restoration of steroids Methods: In a 14 weeks prospective study exhaled VOCs were collected and analysed by GC-MS (baseline corrected and normalized) in 22 patients with mild-moderately severe asthma using inhaled corticosteroids (ICS) at baseline. Then ICS was stopped and the patients were followed up until exacerbation/LOC (Reddel et al, AJRCCM, 2009), and dose restoration. Results: Based on univariate statistical analysis in R, 21 GC-MS fragments were found to be significantly different (p Conclusion: This study reveals reciprocal differences in patterns of exhaled VOCs between clinically stable and unstable states of patients with asthma, suggesting that breath analysis qualifies for non-invasive detection of exacerbations/LOC in monitoring of asthma.

Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whether Aspergillus fumigatus colonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined the A. fumigatus colonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. The P value was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized by A. fumigatus. In total, 3 subjects were misclassified, resulting in a cross-validated accuracy of the Cyranose detecting IA of 89% (P = 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate that A. fumigatus colonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.

Summary Background Fixed airflow limitation can be found both in asthma and chronic obstructive pulmonary disease (COPD), posing a day-to-day diagnostic challenge. Objective We aimed to determine the external validity of metabolomic analysis of exhaled air by electronic nose for distinguishing asthma and COPD in patients with fixed airways obstruction. Methods One hundred patients were included in a cross-sectional design: 60 asthma patients: 21 with fixed airways obstruction (fixed asthma), 39 with reversible airways obstruction (classic asthma) and 40 COPD patients (GOLD stages II–III). Standardized sampling of exhaled breath was performed and volatile organic compounds were captured using an electronic nose resulting in breathprints. External validity in newly recruited patients (validation sets) was tested using a previous and independent training set. Breathprints were analysed by principal component and canonical discriminant analysis and area under the curve (AUC) of receiver operating characteristic curves. Results External validity of breathprints showed 88% accuracy for distinguishing fixed asthma from COPD (AUC 0.95, 95% CI 0.84–1.00, sensitivity 85%, specificity 90%) and 83% for classic asthma (AUC 0.93, 95% CI 0.87–1.00, sensitivity 91%, specificity 90%) (both P

Rationale Chronic obstructive pulmonary disease (COPD) and asthma can exhibit overlapping clinical features. Exhaled air contains volatile organic compounds (VOCs) that may qualify as noninvasive biomarkers. VOC profiles can be assessed using integrative analysis by electronic nose, resulting in exhaled molecular fingerprints (breathprints). Objectives: We hypothesized that breathprints by electronic nose can discriminate patients with COPD and asthma. Methods: Ninety subjects participated in a cross-sectional study: 30 patients with COPD (age, 61.6+/-9.3 years; FEV1, 1.72+/-0.69 L), 20 patients with asthma (age, 35.4+/-15.1 years; FEV1, 3.32+/-0.86 L), 20 nonsmoking control subjects (age, 56.7+/-9.3 years; FEV1, 3.44+/-0.76 L), and 20 smoking control subjects (age, 56.1+/-5.9 years; FEV1, 3.58+/-0.78). After 5 minutes of tidal breathing through an inspiratory VOC filter, an expiratory vital capacity was collected in a Tedlar bag and sampled by electronic nose. Breathprints were analyzed by discriminant analysis on principal component reduction resulting in cross-validated accuracy values (accuracy). Repeatability and reproducibility were assessed by measuring samples in duplicate by two devices. Measurements and Main Results: Breathprints from patients with asthma were separated from patients with COPD (accuracy 96%; P

Rationale The majority of patients suspected of pulmonary embolism (PE) undergoes imaging tests, but PE will only be confirmed in a minority of patients. Consequently, there is a need for non-invasive, safe assessment aimed to accurately exclude PE. Exhaled breath molecular profiling by electronic nose (eNose) provides a metabolomic fingerprint (breathomics) and may be able to fulfill these criteria. Hypothesis We hypothesized that breath fingerprinting by eNose differentiates between confirmed and absent PE in patients with suspected acute PE, and that this differentiation is most pronounced in patients without relevant comorbidities. Methods Patients with suspected PE were prospectively enrolled in this proof-of-principle study. PE was suspected in case of sudden onset/worsening of dyspnea and/or pleuritic chest pain combined with a high clinical probability according to clinical decision rule (CDR: Wells) or D-dimer >500 ug/L. Confirmed PE and non-PE were defined by the presence and absence of filling defects in the pulmonary artery tree on CT. Breath samples were taken within 2 hours of presentation. Patients inhaled through a filter and exhaled into a Tedlar bag. Breath was analyzed using the Cyranose 320 eNose (Smiths Detection, Pasadena, USA) resulting in breathprints. After principal component reduction breathprints were compared using discriminant analysis, and ROC curves were constructed. Results 40 patients with suspected PE; 20 patients with PE (age 52±15 yrs, 13 males) and 20 patients in whom PE was excluded (48±19 yrs, 8 males). Half of the patients (7 PE, 13 non-PE) had comorbidities known to significantly influence the breathprint (4 diabetes, 12 cancer, 2 renal insufficiency, 4 heart failure). Breathprints of PE and non-PE patients were modestly separated (accuracy 70%, p=0.045). The separation was more pronounced in the non-comorbid group (accuracy 85%, 17/20 correctly classified, p=0.008, PPV 0.86, NPV 0.83), and absent in the comorbid group (accuracy 65%, p=0.78). The ROC curves showed an AUC of 0.81 and 0.55, respectively. Combining the probabilities of PE diagnosis based on breathprints and CDR into one algorithm, the AUC in the non-comorbid group reached 0.90, in the comorbid group 0.51 (fig.1). (Figure presented) ROC curves for the predictive value of electronic nose (eNose) breathprints, Wells score and the combination of eNose breathprints and Wells score for diagnosing pulmonary embolism in subjects (A) without and (B) with comorbidity. Conclusion In this proof-of-principle study, breathomics assessment in patients with suspected PE and no comorbidities provides accurate discrimination between patients with and without PE. A large trainingset of non-comorbid PE patients should be build to avoid masking of the signal by other diseases, and this reference standard should be tested in a newly recruited external validation set, followed by a diagnostic management study.

Contains fulltext : 140583.pdf (Publisher’s version ) (Open Access) Abstract Introduction: Classification of COPD is currently based on the presence and severity of airways obstruction. However, this may not fully reflect the phenotypic heterogeneity of COPD in the (ex-) smoking community. We hypothesized that factor analysis followed by cluster analysis of functional, clinical, radiological and exhaled breath metabolomic features identifies subphenotypes of COPD in a community-based population of heavy (ex-) smokers. Methods: Adults between 50-75 years with a smoking history of at least 15 pack-years derived from a random population-based survey as part of the NELSON study underwent detailed assessment of pulmonary function, chest CT scanning, questionnaires and exhaled breath molecular profiling using an electronic nose. Factor and cluster analyses were performed on the subgroup of subjects fulfilling the GOLD criteria for COPD (post-BD FEV1/FVC < 0.70). Results: Three hundred subjects were recruited, of which 157 fulfilled the criteria for COPD and were included in the factor and cluster analysis. Four clusters were identified: cluster 1 (n = 35; 22\%): mild COPD, limited symptoms and good quality of life. Cluster 2 (n = 48; 31\%): low lung function, combined emphysema and chronic bronchitis and a distinct breath molecular profile. Cluster 3 (n = 60; 38\%): emphysema predominant COPD with preserved lung function. Cluster 4 (n = 14; 9\%): highly symptomatic COPD with mildly impaired lung function. In a leave-one-out validation analysis an accuracy of 97.4\% was reached. Conclusions: This unbiased taxonomy for mild to moderate COPD reinforces clusters found in previous studies and thereby allows better phenotyping of COPD in the general (ex-) smoking population.

Objective To investigate whether exhaled breath analysis using an electronic nose can identify differences between inflammatory joint diseases and healthy controls. Methods In a cross-sectional study, the exhaled breath of 21 rheumatoid arthritis (RA) and 18 psoriatic arthritis (PsA) patients with active disease was compared to 21 healthy controls using an electronic nose (Cyranose 320; Smiths Detection, Pasadena, CA, USA). Breathprints were analyzed with principal component analysis, discriminant analysis, and area under curve (AUC) of receiver operating characteristics (ROC) curves. Volatile organic compounds (VOCs) were identified by gas chromatography and mass spectrometry (GC-MS), and relationships between breathprints and markers of disease activity were explored. Results Breathprints of RA patients could be distinguished from controls with an accuracy of 71% (AUC 0.75, 95% CI 0.60–0.90, sensitivity 76%, specificity 67%). Breathprints from PsA patients were separated from controls with 69% accuracy (AUC 0.77, 95% CI 0.61–0.92, sensitivity 72%, specificity 71%). Distinction between exhaled breath of RA and PsA patients exhibited an accuracy of 69% (AUC 0.72, 95% CI 0.55–0.89, sensitivity 71%, specificity 72%). There was a positive correlation in RA patients of exhaled breathprints with disease activity score (DAS28) and number of painful joints. GC-MS identified seven key VOCs that significantly differed between the groups. Conclusions Exhaled breath analysis by an electronic nose may play a role in differential diagnosis of inflammatory joint diseases. Data from this study warrant external validation.

New 'omics'-technologies have the potential to better define airway disease in terms of pathophysiological and clinical phenotyping. The integration of electronic nose (eNose) technology with existing diagnostic tests, such as routine spirometry, can bring this technology to 'point-of-care'. We aimed to determine and optimize the technical performance and diagnostic accuracy of exhaled breath analysis linked to routine spirometry. Exhaled breath was collected in triplicate in healthy subjects by an eNose (SpiroNose) based on five identical metal oxide semiconductor sensor arrays (three arrays monitoring exhaled breath and two reference arrays monitoring ambient air) at the rear end of a pneumotachograph. First, the influence of flow, volume, humidity, temperature, environment, etc, was assessed. Secondly, a two-centre case-control study was performed using diagnostic and monitoring visits in day-to-day clinical care in patients with a (differential) diagnosis of asthma, chronic obstructive pulmonary disease (COPD) or lung cancer. Breathprint analysis involved signal processing, environment correction based on alveolar gradients and statistics based on principal component (PC) analysis, followed by discriminant analysis (Matlab2014/SPSS20). Expiratory flow showed a significant linear correlation with raw sensor deflections (R(2) = 0.84) in 60 healthy subjects (age 43 ± 11 years). No correlation was found between sensor readings and exhaled volume, humidity and temperature. Exhaled data after environment correction were highly reproducible for each sensor array (Cohen's Kappa 0.81-0.94). Thirty-seven asthmatics (41 ± 14.2 years), 31 COPD patients (66 ± 8.4 years), 31 lung cancer patients (63 ± 10.8 years) and 45 healthy controls (41 ± 12.5 years) entered the cross-sectional study. SpiroNose could adequately distinguish between controls, asthma, COPD and lung cancer patients with cross-validation values ranging between 78-88%. We have developed a standardized way to integrate eNose technology with spirometry. Signal processing techniques and environmental background correction ensured that the multiple sensor arrays within the SpiroNose provided repeatable and interchangeable results. SpiroNose discriminated controls and patients with asthma, COPD and lung cancer with promising accuracy, paving the route towards point-of-care exhaled breath diagnostics.