Introduction: Exhaled-breath analysis of volatile organic compounds (VOC) has proven to be effective in the detection and differentiation of various lung diseases.  

Aims and objectives: We explored the use of exhaled-breath analysis with Aeonose to distinguish chronic obstructive pulmonary disease (COPD) from asthma and stable COPD from an acute exacerbation of COPD.

Methods: In a monocentric prospective study 200 patients, 94 patients with COPD (56 stable, 38 with exacerbations) and 106 with asthma (all stable) were included. All patients breathed into an Aeonose device (The eNose Company, Zutphen, the Netherlands) that recorded VOC breath-print pattern. An artificial neural network was trained on exhaled-breath data (at least 25 patients for each condition) and then validated on the other subset of patients. Diagnostic accuracy was presented as area under the receiver operating characteristic curve (AUC-ROC).

Results: Aeonose distinguished COPD (66% male, average age of 65 ± 13 years) from asthma (64% female, average age of 46±15 years) with a sensitivity of 1.0, a specificity of 0.67 and an AUC of 0.84. In the subgroup analysis of COPD patients, exacerbations was distinguished from stable disease with a sensitivity of 0.85, a specificity of 0.58 and an AUC of 0.73.

Conclusion: Analysis of exhaled breath with Aeonose allowed accurate differentiation between patients with COPD and asthma. The differentiation between stable and exacerbated disease in the COPD subgroup was moderate.