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Introduction: Respiratory insufficiency is a leading cause of death in individuals with osteogenesis imperfecta (OI). However, evaluating pulmonary function in OI presents challenges. Commonly used pulmonary function tests such as spirometry and body plethysmography are sometimes difficult to perform for OI patients, and reference intervals are not always applicable. The forced oscillation technique (FOT) is a patient-friendly method for detecting respiratory abnormalities that requires no effort from the patient., Objective: This study investigates the feasibility of FOT in the evaluation of respiratory function in the clinical management of OI patients., Methods: Twelve OI patients, comprising eight with Sillence OI I, two with OI IV, and two with OI III, underwent spirometry, body plethysmography, and FOT, both pre-and post-administration of salbutamol., Results: FOT measurements exhibited consistent trends that aligned with spirometry and body plethysmography findings. The resistance at 8 Hz decreased after the administration of salbutamol, indicating that FOT is able to detect bronchial obstruction and its alleviation by medication ( p  < 0.05). The resonant frequency during expiration was higher than during inspiration in nearly all patients, suggesting obstructive disease. The technique gives insight into both inspiratory and expiratory impairment of pulmonary ventilation. The main FOT parameters showed a relatively high repeatability in duplicate measurements., Conclusion: Bronchial obstruction can be detected by FOT in patients with OI during quiet breathing, making it an easily executable alternative to other lung function measurements. The technique can detect the bronchodilator effect of sympathomimetic medication. It has the potential to provide information on expiratory flow limitation, pulmonary restriction, and reduced lung compliance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Storoni, Verdonk, Micha, Jak, Bugiani, Eekhoff and van den Aardweg.)

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., (Copyright © 2023 The Author(s). 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.)