Exhaled breath analysis by use of eNose technology

Background: Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better non-invasive diagnostic tools are highly needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups.

Methods: In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls (HCs) was analysed using eNose technology (SpiroNose). Statistical analyses were done using Partial Least Square Discriminant Analysis (PLS-DA) and Receiver Operating Characteristic (ROC) analysis. An independent training and validation set (2:1) was used in larger subgroups.

Results: A total of 322 ILD patients and 48 HCs were included; sarcoidosis (n=141), idiopathic pulmonary fibrosis (n=85), ILD associated with connective tissue disease (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic NSIP (n=10), interstitial pneumonia with autoimmune features (n=11), and other ILDs (n=17). eNose sensors discriminated between ILD and HCs, with an AUC of 1.0 in the training and validation set. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85-0.96) in the training set, and an AUC of 0.87 (95% CI 0.77-0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUCs ranging from 0.85 to 0.99.

Conclusion: eNose technology can completely distinguish ILD patients from healthy controls, and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel new biomarker in ILD, enabling timely diagnosis in the future.