Detection of Systemic Sclerosis-Associated Interstitial Lung Disease by Exhaled Breath Analysis Using Electronic Nose Technology

This study investigates the effectiveness of electronic nose (eNose) technology in detecting interstitial lung disease (ILD) among patients with systemic sclerosis (SSc).  SSc is a rare, chronic autoimmune connective tissue disorder characterized by degenerative changes and scarring in the skin, joints, and internal organs and by blood vessel abnormalities. ILD is the leading cause of death in SSc, yet early detection remains challenging. eNose technology analyzes volatile organic compounds (VOCs) in exhaled breath, offering a potential non-invasive tool for timely diagnosis.

Methods

The multicenter, cross-sectional study included 223 patients with confirmed SSc diagnosis, of whom 110 had ILD confirmed via high-resolution CT (HRCT). Exhaled breath was collected using the SpiroNose®, and data were analyzed using partial least squares discriminant analysis (PLS-DA). The study compared breath profiles of SSc patients with and without ILD, and also differentiated SSc-ILD from other types of ILD.

Results

The eNose successfully distinguished SSc-ILD from SSc without ILD, achieving an area under the curve (AUC) of 0.79 in the training set and 0.84 in the test set. When comparing SSc-ILD with other forms of ILD, the AUC was 0.87 in the training set and 0.84 in the test set. The technology showed consistent accuracy across patient subgroups, including those with known risk factors for ILD (e.g., anti-Scl70 positivity and diffuse skin involvement).

Discussion