Clinical and inflammatory phenotyping by breathomics in chronic airway diseases irrespective of the diagnostic label

Abstract

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=0.581) and neutrophilic (R=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.

Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools

“…In addition, we did not include lung imaging, such as CT scan findings, another modality for measuring COPD severity. [17] Finally, while this does not include exhaled breathomics, [18] this extraction tool could be used to assist recruitment for COPD patients into subsequent studies, with potential candidates identified according to severity of FEV1.…”

Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath

“…Exhaled breath analysis was carried out in duplicate with a 2-minute interval by eNose technology (SpiroNose) on the same day as the spirometry tests. The SpiroNose is a technically and clinically validated integration between routine spirometry and eNose technology, allowing stratification of patients in the doctor's office [12,14]. The SpiroNose has seven cross-reactive metal-oxide semiconductor sensors (sensors 1-7) for in duplicate sampling of exhaled air and the same set of sensors sampling ambient air for background correction (supplementary Figure S1 and Table S1, available at Annals of Oncology online).…”

“…The SpiroNose has seven cross-reactive metal-oxide semiconductor sensors (sensors 1-7) for in duplicate sampling of exhaled air and the same set of sensors sampling ambient air for background correction (supplementary Figure S1 and Table S1, available at Annals of Oncology online). From each sensor signal, two variables are determined: (i) the highest sensor peak normalized to the most stable sensor, sensor 2, to minimize interarray differences and (ii) the ratio between the sensor peak and the breath hold (BH) point [14]. A detailed description of the measurement, set-up and the everyday verification of the sensor stability is included in the supplementary material, available at Annals of Oncology online.…”

“…The processing of the eNose sensor signals including filtering, detrending, ambient correction and peak detection was automatically carried out by the standard eNose software as was previously published [12,14]. The signal processing resulted in a .csv file containing the selected parameters (sensor peak-and peak/BH ratios) serving as the source document for statistical analysis.…”

“…It has been shown that by using eNoses lung cancer [9][10][11][12] and the epidermal growth factor receptor (EGFR) mutation [13] can be detected with relatively high accuracy values. In addition, we have recently shown that exhaled breath analysis allows clinical and inflammatory phenotyping of chronic airway diseases at the point-of-care and may therefore facilitate personalized anti-inflammatory strategies [14].…”