Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath.

BACKGROUND: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. RESEARCH QUESTION: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? STUDY DESIGN AND METHODS: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. RESULTS: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). INTERPRETATION: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

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

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 (R2=0.581) and neutrophilic (R2=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.

Utility of nitric oxide for the diagnosis of asthma in an allergy clinic population.

Fractionated exhaled nitric oxide (FeNO) expression is increased in airway inflammation and several studies have suggested that FeNO measurement can be useful in patients with asthma. Atopic individuals have increased FeNO levels, indicating that atopy may be a codeterminant in FeNO production. The aim of this study was to determine the discriminative value of FeNO for asthma and other atopic conditions in the general allergy clinic. Patients referred to the outpatient allergy clinic were screened. A standardized questionnaire was taken and atopic status was assessed (skin-prick test or specific plasma IgE). FeNO level and spirometry were measured. If the patient's history was suspect for asthma, a provocative concentration causing a 20% decrease in forced expiratory volume in 1 second (PC(20)) histamine challenge followed. One hundred fourteen steroid-naive patients were included. Forty-two subjects were diagnosed as asthmatic patients and 72 were diagnosed as nonasthmatic patients, comprising patients with allergic rhinitis (n = 32), nonallergic rhinitis (n = 11), urticaria (n = 11), eczema (n = 7), and other (n = 11). Asthmatic patients had a higher FeNO level than nonasthmatic patients (44 ppb versus 17 ppb; p < 0.001). Receiver operating characteristic curve analysis revealed the optimal FeNO level to distinguish asthma from nonasthma at 27 ppb, with a sensitivity of 78%, specificity of 92%, a positive predictive value of 86%, and a negative predictive value of 87%. Increased FeNO was positively correlated with the presence of respiratory symptoms (p < 0.01), airflow reversibility (p < 0.001), total IgE (p < 0.001), and negatively correlated with PC(20) histamine (p = 0.019). Multivariate analysis revealed that atopy was not a significant predictor of FeNO in asthmatic patients. Measuring FeNO is a simple and useful test to differentiate new asthma patients from those with other atopic conditions in a general allergy clinic.