Diagnosis | | | |
Dragonieri (2007) [40] | Cross-sectional | e-nose (Cyranose 320) GCMS |
e-nose breathprints accurately discriminate asthma from age-matched controls Less successful at discriminating mild and severe asthma Explorative GCMS analysis identified compounds in asthma
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Fens (2009) [41] | Cross-sectional | e-nose (Cyranose 320) |
e-nose breathprints accurately discriminate asthma from COPD (smoking and nonsmoking as well as ICS-treated and non-ICS-treated) e-nose breathprints accurately discriminate asthma from nonsmoking controls
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Montuschi (2010) [42] | Cross-sectional | e-nose (Tor Vergata) GCMS |
e-nose breathprints accurately discriminate asthma from healthy controls e-nose superior to FeNO and spirometry for diagnostic accuracy Late expiratory phase breath sampling (described as “alveolar air”) gives better separation than mixed expiratory for the e-nose No correlation between e-nose and FeNO or spirometry
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Fens (2011) [43] | Cross-sectional# | e-nose (Cyranose 320) |
e-nose breathprints accurately discriminate asthma with fixed airways obstruction and reversible airway obstruction from COPD Less successful at discriminating fixed airways obstruction from reversible airways obstruction
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Timms (2012) [34] | Cross-sectional | e-nose (Cyranose 320) |
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de Vries (2015) [21] | Cross-sectional | e-nose (Spironose) |
e-nose breathprints accurately discriminate asthma from COPD and healthy controls Less successful at discriminating asthma from lung cancer No significant difference in breathprints from asthma patients at different sites
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Dragonieri (2019) [44] | Cross-sectional# | e-nose (Cyranose 320) |
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Phenotyping | | | |
Ibrahim (2011) [45] | Cross-sectional | GCMS |
VOC model accurately discriminates asthma from healthy controls VOC model accurately discriminates (using sputum granulocyte percentages) eosinophilic from non-eosinophilic asthma (superior to FeNO) and neutrophilic from non-neutrophilic asthma phenotypes VOC model accurately discriminates controlled from uncontrolled asthma (using ACQ score)
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Meyer (2014) [37] | Cross-sectional | GC ToF MS |
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Plaza (2015) [46] | Cross-sectional | e-nose (Cyranose 320) |
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Fens (2015) [39] | Cross-sectional | e-nose (Cyranose 320) |
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Brinkman (2019) [47] | Longitudinal and cross-sectional# | e-nose (composite platform) |
Identified 3 clusters (using e-nose breathprints) with significant differences in chronic OCS usage and blood eosinophil and blood neutrophil percentages The majority of patients had migrated clusters at follow-up; patients that migrated clusters had changes in their sputum eosinophils
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Schleich (2019) [38] | Cross-sectional# | GC ToF MS |
VOC model accurately discriminates (using sputum granulocyte percentages) eosinophilic from paucigranular asthma, eosinophilic from neutrophilic asthma and neutrophilic from paucigranular asthma VOC model accurately discriminates eosinophilic from non-eosinophilic asthma (with similar accuracy to FeNO and blood eosinophils) and neutrophilic from non-neutrophilic asthma VOC model unable to discriminate smokers, ex-smokers and nonsmokers VOC model unable to discriminate ICS-treated and ICS-naïve patients
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Treatment stratification | | | |
van der Schee (2013) [36] | Longitudinal and cross-sectional | e-nose (Cyranose 320) |
e-nose breathprints accurately discriminate asthma from healthy controls (maintained after asthma patients treated with oral prednisolone) Accurately discriminates (at the point of full treatment withdrawal) patients who had lost control from ICS withdrawal from patients who had not Accurately discriminates (at the point of full treatment withdrawal) patients who were OCS-responsive from OCS-unresponsive patients e-nose breathprint correlates with sputum eosinophils
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Treatment monitoring | | | |
Paredi (2000) [48] | Cross-sectional | GC FID |
Exhaled ethane levels increased in asthma patients not receiving steroid therapy compared to steroid treated asthma patients and controls Ethane concentrations increased in patients with more severe bronchoconstriction and gas trapping
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Bruce (2009) [35] | Cross-sectional | Breath ethanol device (Alcometer sd-400TM) |
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Brinkman (2018) [49] | Longitudinal and cross-sectional# | GC ToF MS |
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Exacerbation assessment/prediction | | | |
Olopade (1997) [33] | Longitudinal and cross-sectional | GC FID |
Exhaled pentane concentrations increased in acute exacerbation compared to healthy controls (higher in those requiring admission from the emergency department) Pentane concentration significantly decreased following treatment to concentrations similar to concentrations found in healthy controls and stable (outpatient) asthma
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Brinkman (2017) [50] | Longitudinal | e-nose (composite platform) and GCMS |
VOC model discriminates baseline from loss of control and loss of control from recovery e-nose breathprint accurately discriminates baseline from loss of control and loss of control from recovery GCMS identified compounds correlated with sputum eosinophil but not sputum neutrophils; e-nose breathprints did not correlate with either
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Main findings not applicable to clinical categories | | | |
Lazar (2010) [51] | Longitudinal and cross-sectional | e-nose (Cyranose 320) |
e-nose breathprint discriminates post-methacholine from baseline and post-salbutamol (also post-methacholine) from baseline, but unable to discriminate post-methacholine and post-salbutamol (also post methacholine) e-nose breathprint discriminates post-saline from baseline and post-saline from post-salbutamol (also post-saline)
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van der Schee (2013) [52] | Cross-sectional | e-nose (Cyranose 320) and GCMS |
Peak intensities of GCMS identified compounds did not show changes related to storage time (of up to 14 days) VOC model and e-nose breathprints discriminate asthma from healthy controls with similar accuracies following different storage times
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Lärstad (2007) [53]
| Cross-sectional | GC FID |
|