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Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver-operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66-0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43-0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49-0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50-0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Background: Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma., Objective: We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma., Methods: Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics., Results: Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics., Conclusion: eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes., Objectives: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes., Methods: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability., Results: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045)., Conclusions: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma., (Copyright © 2018 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Asthma and chronic obstructive pulmonary disease (COPD) are complex and overlapping diseases that include inflammatory phenotypes. Novel anti-eosinophilic/anti-neutrophilic strategies demand rapid inflammatory phenotyping, which might be accessible from exhaled breath.Our objective was to capture clinical/inflammatory phenotypes in patients with chronic airway disease using an electronic nose (eNose) in a training and validation set.This was a multicentre cross-sectional study in which exhaled breath from asthma and COPD patients (n=435; training n=321 and validation n=114) was analysed using eNose technology. Data analysis involved signal processing and statistics based on principal component analysis followed by unsupervised cluster analysis and supervised linear regression.Clustering based on eNose resulted in five significant combined asthma and COPD clusters that differed regarding ethnicity (p=0.01), systemic eosinophilia (p=0.02) and neutrophilia (p=0.03), body mass index (p=0.04), exhaled nitric oxide fraction (p<0.01), atopy (p<0.01) and exacerbation rate (p<0.01). Significant regression models were found for the prediction of eosinophilic (R 2 =0.581) and neutrophilic (R 2 =0.409) blood counts based on eNose. Similar clusters and regression results were obtained in the validation set.Phenotyping a combined sample of asthma and COPD patients using eNose provides validated clusters that are not determined by diagnosis, but rather by clinical/inflammatory characteristics. eNose identified systemic neutrophilia and/or eosinophilia in a dose-dependent manner., Competing Interests: Conflict of interest: Disclosures can be found alongside this article at erj.ersjournals.com, (Copyright ©ERS 2018.)

Continuous glucose monitoring (CGM) can be beneficial in critically ill patients. Current CGM devices rely on subcutaneous or blood plasma glucose measurements and consequently there is an increased risk of infections and the possibility of loss of blood with each measurement. A potential method to continuously and non-invasively measure blood glucose levels is using exhaled breath. A correlation between blood glucose levels and volatile organic compounds (VOCs) in the exhaled breath was already reported. VOCs can be analyzed continuously using a so-called electronic nose (eNose). We hypothesize that continuous exhaled breath analysis using an eNose can be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients. Mechanically ventilated patients whose blood glucose concentration was monitored with a CGM device were eligible. An eNose with four metal oxide sensors was used to continuously measure changes in exhaled breath. After pre-processing the data, several regression models were trained, consisting of: (1) only eNose sensor values; (2) only the 1st and 2nd principal components (PC) of eNose values; (3) eNose sensor values and last known blood glucose value as random effect; (4) 1st and 2nd PC of eNose sensor values and CGM value of one minute ago as fixed effect; (5) CGM value of one minute ago as fixed effect. Model performance was measured using the R 2 value, the akaike information criterion and the Clarke error grid. Twenty-three patients were included in the study and 1165 hours of measurements were collected. Performance was low in models 1, 2 and 3 with a mean R 2 of 0.07 [95%-CI: 0.00-0.28], 0.10 [95%-CI: 0.00-0.40] and 0.30 [0.02-0.79], respectively. Performance in models 4 and 5 was better with a mean R 2 of 0.77 [0.02-1.00]. Subsequently, eNose data in model 4 had no added value over using CGM only in model 5. Continuous exhaled breath analysis using this eNose cannot be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients.

Introduction: Continuous breath analysis by electronic nose (eNose) technology in the intensive care unit (ICU) may be useful in monitoring (patho) physiological changes. However, the application of breath monitoring in a non-controlled clinical setting introduces noise into the data. We hypothesized that the sensor signal is influenced by: (1) humidity in the side-stream; (2) patient-ventilator disconnections and the nebulization of medication; and (3) changes in ventilator settings and the amount of exhaled CO₂. We aimed to explore whether the aforementioned factors introduce noise into the signal, and discuss several approaches to reduce this noise., Methods: Study in mechanically-ventilated ICU patients. Exhaled breath was monitored using a continuous eNose with metal oxide sensors. Linear (mixed) models were used to study hypothesized associations., Results: In total, 1251 h of eNose data were collected. First, the initial 15 min of the signal was discarded. There was a negative association between humidity and Sensor 1 (Fixed-effect β: -0.05 ± 0.002) and a positive association with Sensors 2-4 (Fixed-effect β: 0.12 ± 0.001); the signal was corrected for this noise. Outliers were most likely due to noise and therefore removed. Sensor values were positively associated with end-tidal CO₂, tidal volume and the pressure variables. The signal was corrected for changes in these ventilator variables after which the associations disappeared., Conclusion: Variations in humidity, ventilator disconnections, nebulization of medication and changes of ventilator settings indeed influenced exhaled breath signals measured in ventilated patients by continuous eNose analysis. We discussed several approaches to reduce the effects of these noise inducing variables.

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.

Currently, many different methods are being used for pre-processing, statistical analysis and validation of data obtained by electronic nose technology from exhaled air. These various methods, however, have never been thoroughly compared. We aimed to empirically evaluate and compare the influence of different dimension reduction, classification and validation methods found in published studies on the diagnostic performance in several datasets. Our objective was to facilitate the selection of appropriate statistical methods and to support reviewers in this research area. We reviewed the literature by searching Pubmed up to the end of 2014 for all human studies using an electronic nose and methodological quality was assessed using the QUADAS-2 tool tailored to our review. Forty-six studies were evaluated regarding the range of different approaches to dimension reduction, classification and validation. From forty-six reviewed articles only seven applied external validation in an independent dataset, mostly with a case-control design. We asked their authors to share the original datasets with us. Four of the seven datasets were available for re-analysis. Published statistical methods for eNose signal analysis found in the literature review were applied to the training set of each dataset. The performance (area under the receiver operating characteristics curve (ROC-AUC)) was calculated for the training cohort (in-set) and after internal validation (leave-one-out cross validation). The methods were also applied to the external validation set to assess the external validity of the performance. Risk of bias was high in most studies due to non-random selection of patients. Internal validation resulted in a decrease in ROC-AUCs compared to in-set performance:  -0.15,-0.14,-0.1,-0.11 in dataset 1 through 4, respectively. External validation resulted in lower ROC-AUC compared to internal validation in dataset 1 (-0.23) and 3 (-0.09). ROC-AUCs did not decrease in dataset 2 (+0.07) and 4 (+0.04). No single combination of dimension reduction and classification methods gave consistent results between internal and external validation sets in this sample of four datasets. This empirical evaluation showed that it is not meaningful to estimate the diagnostic performance on a training set alone, even after internal validation. Therefore, we recommend the inclusion of an external validation set in all future eNose projects in medicine.

Background: Sarcoidosis is a systemic granulomatous disease of unknown cause that affects the lungs in over 90% of cases. Breath analysis by electronic nose technology provides exhaled molecular profiles that have potential in the diagnosis of several respiratory diseases., Objectives: We hypothesized that exhaled molecular profiling may distinguish well-characterized patients with sarcoidosis from controls. To that end we performed electronic nose measurements in untreated and treated sarcoidosis patients and in healthy controls., Methods: 31 sarcoidosis patients (11 patients with untreated pulmonary sarcoidosis [age: 48.4 ± 9.0], 20 patients with treated pulmonary sarcoidosis [age: 49.7 ± 7.9]) and 25 healthy controls (age: 39.6 ± 14.1) participated in a cross-sectional study. Exhaled breath was collected twice using a Tedlar bag by a standardized method. Both bags were then sampled by an electronic nose (Cyranose C320), resulting in duplicate data. Statistical analysis on sensor responses was performed off-line by principal components (PC) analyses, discriminant analysis and ROC curves., Results: Breathprints from patients with untreated pulmonary sarcoidosis were discriminated from healthy controls (CVA: 83.3%; AUC 0.825). Repeated measurements confirmed those results. Patients with untreated and treated sarcoidosis could be less well discriminated (CVA 74.2%), whereas the treated sarcoidosis group was undistinguishable from controls (CVA 66.7%), Conclusion: Untreated patients with active sarcoidosis can be discriminated from healthy controls. This suggests that exhaled breath analysis has potential for diagnosis and/or monitoring of sarcoidosis., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Although the high mortality rate of pulmonary invasive aspergillosis (IA) in patients with prolonged chemotherapy-induced neutropenia (PCIN) can be reduced by timely diagnosis, a diagnostic test that reliably detects IA at an early stage is lacking. We hypothesized that an electronic nose (eNose) could fulfill this need. An eNose can discriminate various lung diseases through the analysis of exhaled volatile organic compounds (VOCs). An eNose is cheap and noninvasive and yields results within minutes. In a single-center prospective cohort study, we included patients who were treated with chemotherapy expected to result in PCIN. Based on standardized indications, a full diagnostic workup was performed to confirm invasive aspergillosis or to rule it out. Patients with no aspergillosis were considered controls, and patients with probable or proven aspergillosis were considered index cases. Exhaled breath was examined with a Cyranose 320 (Smith Detections, Pasadena, CA). The resulting data were analyzed using principal component reduction. The primary endpoint was cross-validated diagnostic accuracy, defined as the percentage of patients correctly classified using the leave-one-out method. Accuracy was validated by 100,000 random classifications. We included 46 subjects who underwent 16 diagnostic workups, resulting in 6 cases and 5 controls. The cross-validated accuracy of the eNose in diagnosing IA was 90.9% (P = 0.022; sensitivity, 100%; specificity, 83.3%). Receiver operating characteristic analysis showed an area under the curve of 0.93. These preliminary data indicate that PCIN patients with IA have a distinct exhaled VOC profile that can be detected with eNose technology. The diagnostic accuracy of the eNose for invasive aspergillosis warrants validation.

Background The acute respiratory distress syndrome (ARDS) is a common, devastating complication of critical illness that is characterized by pulmonary injury and inflammation. The clinical diagnosis may be improved by means of objective biological markers. Electronic nose (eNose) technology can rapidly and non-invasively provide breath prints, which are profiles of volatile metabolites in the exhaled breath. We hypothesized that breath prints could facilitate accurate diagnosis of ARDS in intubated and ventilated intensive care unit (ICU) patients. Methods Prospective single-center cohort study with training and temporal external validation cohort. Breath of newly intubated and mechanically ventilated ICU-patients was analyzed using an electronic nose within 24 hours after admission. ARDS was diagnosed and classified by the Berlin clinical consensus definition. The eNose was trained to recognize ARDS in a training cohort and the diagnostic performance was evaluated in a temporal external validation cohort. Results In the training cohort (40 patients with ARDS versus 66 controls) the diagnostic model for ARDS showed a moderate discrimination, with an area under the receiver-operator characteristic curve (AUC-ROC) of 0.72 (95%-confidence interval (CI): 0.63-0.82). In the external validation cohort (18 patients with ARDS versus 26 controls) the AUC-ROC was 0.71 [95%-CI: 0.54 - 0.87]. Restricting discrimination to patients with moderate or severe ARDS versus controls resulted in an AUC-ROC of 0.80 [95%-CI: 0.70 - 0.90]. The exhaled breath profile from patients with cardiopulmonary edema and pneumonia was different from that of patients with moderate/severe ARDS. Conclusions An electronic nose can rapidly and non-invasively discriminate between patients with and without ARDS with modest accuracy. Diagnostic accuracy increased when only moderate and severe ARDS patients were considered. This implicates that breath analysis may allow for rapid, bedside detection of ARDS, especially if our findings are reproduced using continuous exhaled breath profiling. [ABSTRACT FROM AUTHOR]

Molecular profiling of exhaled volatile organic compounds (VOC) by electronic nose technology provides breathprints that discriminate between patients with different inflammatory airway diseases, such as asthma and COPD. However, it is unknown whether this is determined by differences in airway caliber. We hypothesized that breathprints obtained by electronic nose are independent of acute changes in airway caliber in asthma. Ten patients with stable asthma underwent methacholine provocation (Visit 1) and sham challenge with isotonic saline (Visit 2). At Visit 1, exhaled air was repetitively collected pre-challenge, after reaching the provocative concentration (PC20) causing 20% fall in forced expiratory volume in 1 second (FEV1) and after subsequent salbutamol inhalation. At Visit 2, breath was collected pre-challenge, post-saline and post-salbutamol. At each occasion, an expiratory vital capacity was collected after 5 min of tidal breathing through an inspiratory VOC-filter in a Tedlar bag and sampled by electronic nose (Cyranose 320). Breathprints were analyzed with principal component analysis and individual factors were compared with mixed model analysis followed by pairwise comparisons. Inhalation of methacholine led to a 30.8 ± 3.3% fall in FEV1 and was followed by a significant change in breathprint (p = 0.04). Saline inhalation did not induce a significant change in FEV1, but altered the breathprint (p = 0.01). However, the breathprint obtained after the methacholine provocation was not significantly different from that after saline challenge (p = 0.27). The molecular profile of exhaled air in patients with asthma is altered by nebulized aerosols, but is not affected by acute changes in airway caliber. Our data demonstrate that breathprints by electronic nose are not confounded by the level of airway obstruction. [ABSTRACT FROM AUTHOR]

Background: Exhaled breath contains thousands of volatile organic compounds (VOCs) that could serve as biomarkers of lung disease. Electronic noses can distinguish VOC mixtures by pattern recognition. Objective: We hypothesized that an electronic nose can discriminate exhaled air of patients with asthma from healthy controls, and between patients with different disease severities. Methods: Ten young patients with mild asthma (25.1 ± 5.9 years; FEV1, 99.9 ± 7.7% predicted), 10 young controls (26.8 ± 6.4 years; FEV1, 101.9 ± 10.3), 10 older patients with severe asthma (49.5 ± 12.0 years; FEV1, 62.3 ± 23.6), and 10 older controls (57.3 ± 7.1 years; FEV1, 108.3 ± 14.7) joined a cross-sectional study with duplicate sampling of exhaled breath with an interval of 2 to 5 minutes. Subjects inspired VOC-filtered air by tidal breathing for 5 minutes, and a single expiratory vital capacity was collected into a Tedlar bag that was sampled by electronic nose (Cyranose 320) within 10 minutes. Smellprints were analyzed by linear discriminant analysis on principal component reduction. Cross-validation values (CVVs) were calculated. Results: Smellprints of patients with mild asthma were fully separated from young controls (CVV, 100%; Mahalanobis distance [M-distance], 5.32), and patients with severe asthma could be distinguished from old controls (CVV, 90%; M-distance, 2.77). Patients with mild and severe asthma could be less well discriminated (CVV, 65%; M-distance, 1.23), whereas the 2 control groups were indistinguishable (CVV, 50%; M-distance, 1.56). The duplicate samples replicated these results. Conclusion: An electronic nose can discriminate exhaled breath of patients with asthma from controls but is less accurate in distinguishing asthma severities. Clinical implication: These findings warrant validation of electronic noses in diagnosing newly presented patients with asthma. [Copyright &y& Elsevier]

Abstract: Background: Malignant Pleural Mesothelioma (MPM) is a tumour of the surface cells of the pleura that is highly aggressive and mainly caused by asbestos exposure. Electronic noses capture the spectrum of exhaled volatile organic compounds (VOCs) providing a composite biomarker profile (breathprint). Objective: We tested the hypothesis that an electronic nose can discriminate exhaled air of patients with MPM from subjects with a similar long-term professional exposure to asbestos without MPM and from healthy controls. Methods: 13 patients with a histology confirmed diagnosis of MPM (age 60.9±12.2 year), 13 subjects with certified, long-term professional asbestos exposure (age 67.2±9.8), and 13 healthy subjects without asbestos exposure (age 52.2±16.2) participated in a cross-sectional study. Exhaled breath was collected by a previously described method and sampled by an electronic nose (Cyranose 320). Breathprints were analyzed by canonical discriminant analysis on principal component reduction. Cross-validated accuracy (CVA) was calculated. Results: Breathprints from patients with MPM were separated from subjects with asbestos exposure (CVA: 80.8%, sensitivity 92.3%, specificity 85.7%). MPM was also distinguished from healthy controls (CVA: 84.6%). Repeated measurements confirmed these results. Conclusions: Molecular pattern recognition of exhaled breath can correctly distinguish patients with MPM from subjects with similar occupational asbestos exposure without MPM and from healthy controls. This suggests that breathprints obtained by electronic nose have diagnostic potential for MPM. [Copyright &y& Elsevier]

Summary: Background: Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that may be used as non-invasive markers of lung disease. The electronic nose analyzes VOCs by composite nano-sensor arrays with learning algorithms. It has been shown that an electronic nose can distinguish the VOCs pattern in exhaled breath of lung cancer patients from healthy controls. We hypothesized that an electronic nose can discriminate patients with lung cancer from COPD patients and healthy controls by analyzing the VOC-profile in exhaled breath. Methods: 30 subjects participated in a cross-sectional study: 10 patients with non-small cell lung cancer (NSCLC, [age 66.4±9.0, FEV1 86.3±20.7]), 10 patients with COPD (age 61.4±5.5, FEV1 70.0±14.8) and 10 healthy controls (age 58.3±8.1, FEV1 108.9±14.6). After 5min tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, subjects performed a single vital capacity maneuver to collect dried exhaled air into a Tedlar bag. The bag was connected to the electronic nose (Cyranose 320) within 10min, with VOC-filtered room air as baseline. The smellprints were analyzed by onboard statistical software. Results: Smellprints from NSCLC patients clustered distinctly from those of COPD subjects (cross validation value [CVV]: 85%; M-distance: 3.73). NSCLC patients could also be discriminated from healthy controls in duplicate measurements (CVV: 90% and 80%, respectively; M-distance: 2.96 and 2.26). Conclusion: VOC-patterns of exhaled breath discriminates patients with lung cancer from COPD patients as well as healthy controls. The electronic nose may qualify as a non-invasive diagnostic tool for lung cancer in the future. [Copyright &y& Elsevier]