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Original Research: ASTHMA |

Diagnostic Performance of an Electronic Nose, Fractional Exhaled Nitric Oxide, and Lung Function Testing in Asthma FREE TO VIEW

Paolo Montuschi, MD; Marco Santonico, PhD; Chiara Mondino, MD; Giorgio Pennazza, PhD; Giulia Mantini, BEng; Eugenio Martinelli, PhD; Rosamaria Capuano, BEng; Giovanni Ciabattoni, MD; Roberto Paolesse, PhD; Corrado Di Natale, PhD; Peter J. Barnes, DM, FCCP; Arnaldo D’Amico, PhD
Author and Funding Information

From the Department of Pharmacology (Dr Montuschi), Faculty of Medicine, Catholic University of the Sacred Heart; the Department of Electronic Engineering (Drs Santonico, Martinelli, Paolesse, Di Natale, and D’Amico; and Mss Mantini and Capuano), University of Tor Vergata; Faculty of Engineering (Dr Pennazza), University Campus Bio-Medico; and the Department of Immunodermatology (Dr Mondino), Istituto Dermopatico dell’Immacolata, IDI, Rome, Italy; the Department of Drug Sciences (Dr Ciabattoni), Faculty of Pharmacy, University “G. d’Annunzio,” Chieti, Italy; and the Airway Disease Section (Dr Barnes), Imperial College, School of Medicine, National Heart and Lung Institute, London, England.

Correspondence to: Paolo Montuschi, MD, Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Largo F. Vito, 1, 00168 Rome, Italy; e-mail: pmontuschi@rm.unicatt.it


Funding/Support: Supported by Merck, Sharp, and Dohme, and Catholic University of the Sacred Heart academic grant 2008-2009.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (www.chestpubs.org/site/misc/reprints.xhtml).


© 2010 American College of Chest Physicians


Chest. 2010;137(4):790-796. doi:10.1378/chest.09-1836
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Background:  Analysis of exhaled breath by biosensors discriminates between patients with asthma and healthy subjects. An electronic nose consists of a chemical sensor array for the detection of volatile organic compounds (VOCs) and an algorithm for pattern recognition. We compared the diagnostic performance of a prototype of an electronic nose with lung function tests and fractional exhaled nitric oxide (FENO) in patients with atopic asthma.

Methods:  A cross-sectional study was undertaken in 27 patients with intermittent and persistent mild asthma and in 24 healthy subjects. Two procedures for collecting exhaled breath were followed to study the differences between total and alveolar air. Seven patients with asthma and seven healthy subjects participated in a study with mass spectrometry (MS) fingerprinting as an independent technique for assessing between group discrimination. Classification was based on principal component analysis and a feed-forward neural network.

Results:  The best results were obtained when the electronic nose analysis was performed on alveolar air. Diagnostic performance for electronic nose, FENO, and lung function testing was 87.5%, 79.2%, and 70.8%, respectively. The combination of electronic nose and FENO had the highest diagnostic performance for asthma (95.8%). MS fingerprints of VOCs could discriminate between patients with asthma and healthy subjects.

Conclusions:  The electronic nose has a high diagnostic performance that can be increased when combined with FENO. Large studies are now required to definitively establish the diagnostic performance of the electronic nose. Whether this integrated noninvasive approach will translate into an early diagnosis of asthma has to be clarified.

Trial registration:  EUDRACT https://eudralink.emea.europa.eu; Identifier: 2007-000890-51; and clinicaltrials.gov; Identifier: NCT00819676.

Figures in this Article

Several volatile organic compounds (VOCs) have been identified in exhaled breath in healthy subjects by gas chromatography (GC)/mass spectrometry (MS).1,2 Identification of selective VOC patterns in exhaled breath is potentially useful as a biomarker of asthma.3 Differences between alveolar and oropharyngeal/airway air can affect the results and should be considered when analyzing VOCs in exhaled breath.4 An electronic nose is an artificial sensor system that consists of an array of chemical sensors for VOC detection and an algorithm for pattern recognition.5-7 The electronic nose discriminates between patients with asthma and healthy subjects,3 between patients with asthma of different severity,3 between patients with lung cancer and healthy subjects,8-10 and between patients with lung cancer and COPD.9

The primary aim of this study was to compare the diagnostic performance of a prototype of an electronic nose with fractional exhaled nitric oxide (FENO), an independent method for assessing airway inflammation, and lung function tests in patients with asthma. Secondary aims of this study were to ascertain whether an electronic nose could discriminate between patients with asthma and healthy subjects and to establish the best sampling protocol (alveolar vs oropharyngeal/airway air) for electronic nose analysis.

Study Subjects

Twenty-seven white patients with intermittent or mild persistent asthma and 24 healthy subjects were studied (Table 1). Among study subjects, seven patients with asthma and seven healthy subjects participated in a study with GC/MS used for MS fingerprinting, an independent technique for assessing between-group discrimination.

Table Graphic Jump Location
Table 1 —Subject Characteristics

Data are expressed as No. or mean ± SEM. FEF25%-75% = forced expiratory flow at 25% to 75% of forced vital capacity.

a 

P < .05 compared with healthy subjects.

b 

P < .01 compared with healthy subjects.

c 

Presence of atopy was confirmed by skin prick testing for common aeroallergens. Patients with asthma had no corticosteroids or other antiinflammatory drugs for asthma within 4 wk.

Patients with asthma were recruited from the Allergy Outpatient Clinic, Istituto Dermopatico dell’Immacolata, IDI, Rome, Italy. Diagnosis and classification of asthma were based on clinical history and examination and pulmonary function parameters according to current guidelines.11 Patients had intermittent asthma with symptoms equal to or less often than twice a week (step 1) or mild persistent asthma with symptoms more often than twice a week (step 2), FEV1 of ≥ 80% of predicted value and reversibility of ≥ 12% to salbutamol, or a positive provocation test result with methacholine or exercise. Patients with asthma had atopy as confirmed by positive skin prick test results in response to common aeroallergens. All atopic subjects had a clinical history of atopy. They were not taking any regular medication, but used inhaled short-acting β2-agonists as needed for symptom relief. Healthy subjects had no history of asthma or atopic disease, negative skin prick test results, and normal spirometry. Subjects had no history of smoking, no upper respiratory tract infections in the previous 3 weeks, and were excluded from the study if they had used corticosteroids or antiinflammatory drugs for asthma in the previous 4 weeks.

Study Design

The type of study was cross-sectional. Subjects, recruited from March 2007 to September 2008, attended on one occasion for clinical examination, FENO measurement, electronic nose analysis, lung function tests, and skin prick testing. Informed consent was obtained from patients. The study was approved by the Ethics Committee of the Catholic University of the Sacred Heart, Rome, Italy.

Pulmonary Function and FENO Measurement

Spirometry was performed with a Pony FX spirometer (Cosmed; Rome, Italy). The best of three consecutive maneuvers were chosen. FENO was measured with the NIOX system (Aerocrine; Stockholm, Sweden) with a single-breath online method at constant flow of 50 mL/s according to American Thoracic Society guidelines.12 FENO measurements were obtained before spirometry.

Collection of Exhaled Breath

Exhaled breath was collected from each subject at 8.30 AM. No food or drinks were allowed at least 2 h prior to collection of exhaled breath. Two procedures for collecting exhaled breath were followed to study the differences between total exhaled breath and alveolar air and to establish the best protocol for the electronic nose analysis. In the first sampling procedure (Fig 1A), subjects were asked to inhale to total lung capacity and to exhale into a mouthpiece connected to a Tedlar bag through a three-way valve. The total exhaled breath was collected. In the second sampling procedure (Fig 1B), subjects were asked to repeat the maneuver. The sampling system was designed considering a dead space volume of 150 mL. The first 150 mL were collected into a separate Tedlar bag and discarded, whereas the remaining exhaled breath, principally derived from the alveolar compartment, was analyzed.

Figure Jump LinkFigure 1. Exhaled breath sampling for electronic nose analysis. (A) Sampling of total exhaled breath. (B) Sampling of alveolar exhaled air. The sampling system was designed considering a dead space volume of 150 mL. The first part of exhalation (150 mL) was discarded, whereas the remaining exhaled breath principally derived from the alveolar compartment was collected for electronic nose analysis.Grahic Jump Location
Electronic Nose

A prototype electronic nose designed at the University of Rome Tor Vergata was used.13,14 The instrument contains an array of eight quartz microbalance gas sensors coated by molecular films of metalloporphyrins.15 Sensors detect the amount of molecules absorbed in a sensitive film through the changes of resonant frequency that is proportional to the absorbed mass.15 The frequency shifts are composed in patterns and analyzed by pattern recognition algorithms.16 Samples were analyzed immediately after their collection. Ambient VOCs were subtracted from measures and results were automatically adjusted for ambient VOCs.

GC/MS

GC/MS used for MS fingerprinting of VOCs17 was performed (1) to confirm between-group differences in VOC patterns detected with an electronic nose, and (2) to ascertain whether exhaled breath samples were stable within 48 h from collection. With MS fingerprinting, the sum of mass spectra of VOCs detected by GC was considered as a sample pattern that was then analyzed like the electronic nose patterns.17 Total exhaled breath was collected (Fig 1A). Samples were immediately absorbed onto sterile 10 × 10 cm2 gauze pads that were sealed in 20-mL headspace glass vials with crimped seal with polytetrafluoroethylene/silicone septa (Supelco; Bellefonte, PA).15 Samples were put on ice, transferred to the laboratory, and stored at −10°C until GC/MS analysis that was performed within 48 h from sample collection. Before solid-phase microextraction, samples were kept at room temperature for 9 h. VOCs were extracted from the vials using a divinylbenzene/carboxen on a polydimethylsiloxane 50/30 m fiber (Supelco; Sigma-Aldrich; St. Louis, MO). Exposure was performed at room temperature for 15 h by piercing the silicon septum and inserting the fiber into the headspace of the vial. Solid-phase microextraction fiber was then removed from the vial and transferred to the injector of the gas chromatograph for thermal desorption.

VOCs adsorbed in the fiber were desorbed in the injection port of the GC for 3 min at an inlet temperature of 250°C in the splitless mode. Mass spectra were obtained using electron ionization. The ionization occurred with a kinetic energy of the impacting electrons of 70 eV.18 Mass spectra and reconstructed chromatograms (total ion current) were acquired in the full scan mode in the mass range m/z 50-100.15

Skin Testing

Atopy was assessed by skin prick tests for common aeroallergens (mixture for house dust mite [Dermatophagoides pteronyssinus and Dermatophagoides farinae], grass pollen [cocksfoot and timothy], tree pollen [birch, ash tree, olive tree, oak, and cypress], weed pollen [Ambrosia artemisifolia and Parietaria officinalis], animal danders [cat and dog allergens], and fungal allergens [Aspergillus species and Alternaria alternata]) (Stallergenes; Antony, France).19 A positive skin test response was defined as a wheal with a mean diameter (mean of maximum and 90° midpoint diameters) of at least 3 mm greater than that produced with a saline control.

Multivariate Data Analysis

The analysis of patterns requires multivariate statistical algorithms.20 We used principal component analysis to represent multidimensional data in a bidimensional plane and feed-forward neural network to classify electronic nose, FENO, and spirometry data. A feed-forward neural network is a biologically derived classification model21 that is formed by a number of processing units (neurons), organized in layers. The total datasets have been divided in training (27 measures) and testing (24 measures) set. To test the presence of any drift in sensors, the first data collected were used for training and the remaining data for testing.

Statistical Analysis

FENO values were expressed as medians and interquartile ranges (25th and 75th percentiles). Spirometry values were expressed as mean ± SEM. Unpaired t test and Mann-Whitney U test were used for comparing groups for normally distributed and nonparametric data, respectively. Correlation was expressed as a Pearson coefficient. Significance was defined as a value of P < .05.

Electronic Nose

The best results were obtained when electronic nose analysis was performed on alveolar air (Tables 2, 3). Diagnostic classification with 95% CIs is shown in Table 4. The diagnostic performance was determined with the test datasets in terms of the number of correct identifications of asthma diagnosis based on current guidelines.11 Diagnostic performance for the electronic nose, FENO, lung function tests, and their combinations is shown in Table 2 and Table 3 and is related to the best performances obtained with the neural network for each specific case. The combination of electronic nose analysis of alveolar air and FENO had the highest diagnostic performance for asthma (95.8%). Electronic nose analysis of alveolar air was able to discriminate between patients with asthma and healthy subjects in 87.5% of cases, a diagnostic performance that was higher than that of FENO (79.2%), spirometry (70.8%), and combination of FENO and spirometry (83.3%). There was no correlation between the electronic nose, FeNO, and lung function testing data in either the asthma or the healthy control group.

Table Graphic Jump Location
Table 2 —Classification Matrices of the Feed-Forward Neural Network Classifier in Training and Testing Phase for Data Related to Electronic Nose, Fractional Exhaled Nitric Oxide, and Spirometry

E-nose analysis was performed in total exhaled air and alveolar exhaled air. E-nose = electric nose; FENO = fractional exhaled nitric oxide.

a 

False negatives.

b 

False positives.

Table Graphic Jump Location
Table 3 —The Classification Matrices of the Feed-Forward Neural Network Classifier in Training and Testing Phase for Data Related to Combination of Electronic Nose, FENO, and Spirometry

Electronic nose analysis was performed in total exhaled air and alveolar exhaled air. See Table 2 for expansion of abbreviations.

a 

False negatives.

b 

False positives.

Table Graphic Jump Location
Table 4 —Diagnostic Classification With 95% of CIs in Training and Testing Phase for Data Related to Combination of Electronic Nose, FENO, and Spirometry

Electronic nose analysis was performed in total exhaled air and alveolar exhaled air. See Table 2 for expansion of abbreviations.

GC/MC

MS fingerprints of VOCs in exhaled breath in patients with asthma were different from those in healthy subjects (Fig 2), confirming that the electronic nose used in this study discriminates between patients with asthma and controls. MS fingerprinting performed within 48 h from sampling was similar to that obtained immediately after collection (data not shown), indicating that samples are stable for at least 48 h.

Figure Jump LinkFigure 2. PC analysis of mass spectrometry fingerprinting of patients with asthma and healthy subjects. PC = principal component.Grahic Jump Location
FENO and Lung Function Tests

Patients with asthma had higher median FENO values than healthy subjects (37. 6 [26.0-61.5] ppb vs 13.4 [10.0-19.9] ppb, P < .0001, respectively) (Fig 3).Both study groups had normal FEV1 values (Table 1). Patients with asthma had lower absolute (P = .032) and percentage of predicted FEV1 values (P = .004) than controls (Table 1). Absolute (P = .003) and percentage of predicted forced expiratory flow between 25% and 75% of forced vital capacity (FEF25%-75%) (P = .002) were lower in patients with asthma than in healthy subjects (Table 1).

Figure Jump LinkFigure 3. FENO concentrations in 27 patients with asthma (■) and 24 healthy subjects (●). Median values are shown with bars. FENO = fractional exhaled nitric oxide.Grahic Jump Location

The original aspects of our study are: (1) the comparison between an electronic nose and FENO, in addition to lung function tests; (2) the comparison between total and alveolar exhaled air; (3) the number of study subjects (27 patients with intermittent and persistent mild asthma and 24 healthy controls); (4) the MS fingerprinting based on GC/MS analysis; and (5) the analysis of data based on a neural network that included a training and test analysis performed in two separate datasets for stringent quality control.

We compared the diagnostic performance of an electronic nose, FENO, and lung function testing in patients with a physician-based diagnosis of asthma. The combination of electronic nose and FENO had the best diagnostic performance for asthma (95.7%). When alveolar exhaled air was sampled, the electronic nose had a diagnostic performance of 87.5%, higher than that observed with FENO (79.2%), spirometry (70.8%), and their combination (83.3%). Spirometry had the lowest diagnostic performance in line with a previous prospective study that compared FENO, sputum eosinophils, and FEV1 in children with asthma.22 The present study confirms that FENO has a good diagnostic performance for asthma. The higher diagnostic performance of electronic nose observed in our study might reflect the fact that electronic nose analyzes patterns generated by a complex mixture of VOCs in exhaled breath, whereas the NO analyzer detects only one biomolecule. A limitation of our study is the relatively small number of subjects, which precludes definitive conclusions on asthma diagnostic performance of electronic nose, FENO and lung function testing. Large-powered studies are required to definitively establish the diagnostic performance of these techniques in patients with a known diagnosis of asthma. We were unable to ascertain whether the electronic nose can be used as a diagnostic tool for screening of patients with asthma that requires large prospective studies.

MS fingerprinting confirms data obtained with the electronic nose and further supports its use for asthma diagnosis. Identification and quantitative GC/MS analysis of VOCs in exhaled breath are required to establish the pathophysiological role of single VOCs in asthma. Taken together, these data indicate that electronic nose might be useful for asthma diagnosis, particularly in combination with FENO. Our results are consistent with a previous study showing that a different electronic nose discriminates between patients with asthma and healthy subjects3 and prospect the possibility of using different noninvasive techniques for achieving a greater asthma diagnostic performance.

Because asthma is principally characterized by airway inflammation, it may seem surprising that the best results with the electronic nose were obtained when collecting alveolar air rather than total exhaled breath that includes exhaled breath from the airways. This might reflect the contribution of oropharyngeal air to total breath.4 A previous study with GC/MS has shown that for 47 VOCs very significant differences in concentrations/detection were recorded between alveolar and oropharyngeal air.4 When sampling total breath, oropharyngeal air is likely to introduce confounding factors that make electronic nose analysis less reflective of what occurs in the respiratory system. Because of significant interindividual variability in dead space volume, it is likely that mixed airways/alveolar air rather than alveolar air was collected in our study. For this reason, the results of electronic nose analysis of alveolar air could partially reflect the production of VOCs within the peripheral airways.

The lack of correlation between electronic nose results and FENO might indicate that these techniques reflect different aspects of airway inflammation. Formal studies to compare electronic nose analysis with independent biomarkers of airway inflammation (eg, FENO, sputum eosinophils)12,23,24 and to ascertain whether the electronic nose could be used for assessing and monitoring airway inflammation in patients with asthma are warranted.

The electronic nose is not suitable for ascertaining the cellular source of VOCs in exhaled breath that requires bronchoscopy studies. Chronic airway inflammation can modify the metabolic pathways in patients with asthma as reflected by increased concentrations of exhaled pentane, one of the end products of lipid peroxidation.25 Because patients included in our study were not on regular antiinflammatory drugs for asthma, we were unable to assess the effect of pharmacological therapy on VOC patterns in exhaled breath for which controlled studies are required. Both the present and a previous study3 aimed at ascertaining whether electronic nose could discriminate between atopic patients with asthma and healthy nonatopic subjects. For this reason, these studies were unable to assess the contribution of atopy itself to the classification based on the electronic nose. A control group of atopic subjects without asthma should be included in future studies to clarify whether, and to what extent, atopy is responsible for the selective VOC patterns in exhaled breath observed in patients with atopic asthma.

When analyzing electronic nose data, validation of the statistical model used for classification is essential. In contrast to a previous study in which the same dataset was used for training the model and classification,3 we used two different datasets for training and testing that were obtained in different periods of time, reducing the possibility of introducing a temporal bias in the model. This way, the predictive capacity of the classification model is likely to be more suitable for a real-life situation.

In conclusion, the electronic nose discriminates between patients with asthma and healthy subjects and its performance is increased when combined with FENO. Large studies are required to definitively establish the diagnostic performance of the electronic nose. Whether this integrated noninvasive approach will translate into an early diagnosis of asthma has to be clarified.

Author contributions: Dr Montuschi: contributed to study planning, study design, measurement of FENO, spirometry, data analysis, data interpretation, and wrote the manuscript.

Dr Santonico: contributed to electronic nose analysis, data interpretation, and mass spectrometry fingerprinting.

DrMondino: contributed to recruitment of patients and skin prick testing.

DrPennazza: contributed to electronic nose analysis, data interpretation, and mass spectrometry fingerprinting.

Ms Mantini: contributed to electronic nose analysis and mass spectrometry fingerprinting.

Dr Martinelli: contributed to multivariate data analysis and manuscript preparation.

Ms Capuano: contributed to electronic nose analysis and mass spectrometry fingerprinting.

Dr Ciabattoni: contributed to manuscript preparation and revision.

Dr Paolesse: contributed to biosensor manufacture.

Dr Di Natale: contributed to electronic nose setup, data interpretation, and manuscript preparation.

Dr Barnes: contributed to manuscript preparation and revision.

Dr D’Amico: contributed to electronic nose setup, data interpretation, and manuscript preparation and revision.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Barnes receives research funding from and has been on Scientific Advisory Boards for AstraZeneca, Boehringer-Ingelheim, Chiesi, GlaxoSmithKline, Novartis, Pfizer, and UCB. Drs Montuschi, Santonico, Mondino, Pennazza, Martinelli, Ciabattoni, Paolesse, Di Natale, D’Amico, and Mss Mantini and Capuano have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Other contributions: This work was performed at the Catholic University of the Sacred Heart, Rome, Italy.

FEF25%-75%

forced expiratory flow at 25% to 75% of forced vital capacity

FENO

fractional exhaled nitric oxide

GC

gas chromatography

MS

mass spectrometry

VOC

volatile organic compound

Phillips M. Method for the collection and assay of volatile organic compounds in breath. Anal Biochem. 1997;2472:272-278. [CrossRef] [PubMed]
 
Phillips M, Herrera J, Krishnan S, Zain M, Greenberg J, Cataneo RN. Variation in volatile organic compounds in the breath of normal humans. J Chromatogr B Biomed Sci Appl. 1999;7291-2:75-88. [CrossRef] [PubMed]
 
Dragonieri S, Schot R, Mertens BJ, et al. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol. 2007;1204:856-862. [CrossRef] [PubMed]
 
van den Velde S, Quirynen M, van Hee P, van Steenberghe D. Differences between alveolar air and mouth air. Anal Chem. 2007;799:3425-3429. [CrossRef] [PubMed]
 
Turner AP, Magan N. Electronic noses and disease diagnostics. Nat Rev Microbiol. 2004;22:161-166. [CrossRef] [PubMed]
 
Röck F, Barsan N, Weimar U. Electronic nose: current status and future trends. Chem Rev. 2008;1082:705-725. [CrossRef] [PubMed]
 
D’Amico A, Di Natale C, Paolesse R, et al. Olfactory systems for medical applications. Sens Actuators B Chem. 2008;1301:458-465. [CrossRef]
 
Machado R, Laskowski D, Deffenderfer O, et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med. 2005;17111:1286-1291. [CrossRef] [PubMed]
 
Dragonieri S, Annema JT, Schot R, et al. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer. 2009;642:166-170. [CrossRef] [PubMed]
 
Di Natale C, Macagnano A, Martinelli E, et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron. 2003;1810:1209-1218. [CrossRef] [PubMed]
 
National Asthma Education and Prevention ProgramNational Asthma Education and Prevention Program Expert Panel Report III. Guidelines for the Diagnosis and Management of Asthma. 2007; Bethesda, MD National Heart, Lung, and Blood Institute NIH publication 08-5847.
 
Recommendations for standardized procedures for the on-line and off-line measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide in adults and children-1999. Am J Respir Crit Care Med. 1999;1606:2104-2117. [PubMed]
 
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D’Amico A, Di Natale C, Paolesse R, Mantini A, Macagnano A. Metalloporphyrins as basic material for volatile sensitive sensors. Sens Actuators B Chem. 2000;651-3:209-215. [CrossRef]
 
D’Amico A, Bono R, Pennazza G, et al. Identification of melanoma with a gas sensor array. Skin Res Technol. 2008;142:226-236. [CrossRef] [PubMed]
 
Gardner J, Bartlett P. A brief history of electronic noses. Sens Actuators B Chem. 1994;181-3:211-220
 
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Kusch P, Knupp G. Headspace-SPME-GC-MS Identification of volatile organic compounds released from expanded polystyrene. Journal of Polymers and the Environment. 2004;122:83-87. [CrossRef]
 
Montuschi P, Mondino C, Koch P, Barnes PJ, Ciabattoni G. Effects of a leukotriene receptor antagonist on exhaled leukotriene E4and prostanoids in children with asthma. J Allergy Clin Immunol. 2006;1182:347-353. [CrossRef] [PubMed]
 
Johnson R, Wichern D. Applied Multivariate Statistical Analysis. 1992;3rd ed. New York, NY Prentice Hall
 
Bishop CM. Pattern Recognition and Machine Learning. 2006; Heidelberg, Germany Springer
 
Sivan Y, Gadish T, Fireman E, Soferman R. The use of exhaled nitric oxide in the diagnosis of asthma in school children. J Pediatr. 2009;1552:211-216. [CrossRef] [PubMed]
 
Malerba M, Ragnoli B, Radaeli A, Tantucci C. Usefulness of exhaled nitric oxide and sputum eosinophils in the long-term control of eosinophilic asthma. Chest. 2008;1344:733-739. [CrossRef] [PubMed]
 
Petsky HL, Kynaston JA, Turner C, et al. Tailored interventions based on sputum eosinophils versus clinical symptoms for asthma in children and adults. Cochrane Database Syst Rev. 2007;2:CD005603
 
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Figures

Figure Jump LinkFigure 1. Exhaled breath sampling for electronic nose analysis. (A) Sampling of total exhaled breath. (B) Sampling of alveolar exhaled air. The sampling system was designed considering a dead space volume of 150 mL. The first part of exhalation (150 mL) was discarded, whereas the remaining exhaled breath principally derived from the alveolar compartment was collected for electronic nose analysis.Grahic Jump Location
Figure Jump LinkFigure 2. PC analysis of mass spectrometry fingerprinting of patients with asthma and healthy subjects. PC = principal component.Grahic Jump Location
Figure Jump LinkFigure 3. FENO concentrations in 27 patients with asthma (■) and 24 healthy subjects (●). Median values are shown with bars. FENO = fractional exhaled nitric oxide.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Subject Characteristics

Data are expressed as No. or mean ± SEM. FEF25%-75% = forced expiratory flow at 25% to 75% of forced vital capacity.

a 

P < .05 compared with healthy subjects.

b 

P < .01 compared with healthy subjects.

c 

Presence of atopy was confirmed by skin prick testing for common aeroallergens. Patients with asthma had no corticosteroids or other antiinflammatory drugs for asthma within 4 wk.

Table Graphic Jump Location
Table 2 —Classification Matrices of the Feed-Forward Neural Network Classifier in Training and Testing Phase for Data Related to Electronic Nose, Fractional Exhaled Nitric Oxide, and Spirometry

E-nose analysis was performed in total exhaled air and alveolar exhaled air. E-nose = electric nose; FENO = fractional exhaled nitric oxide.

a 

False negatives.

b 

False positives.

Table Graphic Jump Location
Table 3 —The Classification Matrices of the Feed-Forward Neural Network Classifier in Training and Testing Phase for Data Related to Combination of Electronic Nose, FENO, and Spirometry

Electronic nose analysis was performed in total exhaled air and alveolar exhaled air. See Table 2 for expansion of abbreviations.

a 

False negatives.

b 

False positives.

Table Graphic Jump Location
Table 4 —Diagnostic Classification With 95% of CIs in Training and Testing Phase for Data Related to Combination of Electronic Nose, FENO, and Spirometry

Electronic nose analysis was performed in total exhaled air and alveolar exhaled air. See Table 2 for expansion of abbreviations.

References

Phillips M. Method for the collection and assay of volatile organic compounds in breath. Anal Biochem. 1997;2472:272-278. [CrossRef] [PubMed]
 
Phillips M, Herrera J, Krishnan S, Zain M, Greenberg J, Cataneo RN. Variation in volatile organic compounds in the breath of normal humans. J Chromatogr B Biomed Sci Appl. 1999;7291-2:75-88. [CrossRef] [PubMed]
 
Dragonieri S, Schot R, Mertens BJ, et al. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol. 2007;1204:856-862. [CrossRef] [PubMed]
 
van den Velde S, Quirynen M, van Hee P, van Steenberghe D. Differences between alveolar air and mouth air. Anal Chem. 2007;799:3425-3429. [CrossRef] [PubMed]
 
Turner AP, Magan N. Electronic noses and disease diagnostics. Nat Rev Microbiol. 2004;22:161-166. [CrossRef] [PubMed]
 
Röck F, Barsan N, Weimar U. Electronic nose: current status and future trends. Chem Rev. 2008;1082:705-725. [CrossRef] [PubMed]
 
D’Amico A, Di Natale C, Paolesse R, et al. Olfactory systems for medical applications. Sens Actuators B Chem. 2008;1301:458-465. [CrossRef]
 
Machado R, Laskowski D, Deffenderfer O, et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med. 2005;17111:1286-1291. [CrossRef] [PubMed]
 
Dragonieri S, Annema JT, Schot R, et al. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer. 2009;642:166-170. [CrossRef] [PubMed]
 
Di Natale C, Macagnano A, Martinelli E, et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron. 2003;1810:1209-1218. [CrossRef] [PubMed]
 
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