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Diagnosis of interstitial lung diseases using eNose technology

Introduction: Early and accurate diagnosis is a major challenge in interstitial lung diseases (ILD). Better non-invasive diagnostic tools are highly needed. We aimed to assess the reliability of exhaled breath analysis using eNose technology to discriminate between ILD and healthy controls, and to distinguish ILD subgroups. Methods: In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls (HC) was collected and analyzed using eNose technology (SpiroNose). Sensor data were analyzed with partial least squares-discriminant analysis and ROC analysis in an independent training and validation set (2:1). Results: A total of 48 HCs and 322 ILD patients were included: 83 IPF, 141 sarcoidosis, 33 CTD-ILD, 25 chronic hypersensitivity pneumonitis (CHP), 10 idiopathic NSIP and 30 other ILDs. SpiroNose sensors perfectly discriminated between ILDs and HCs (AUC 1.0, Figure 1A). Within the ILD group, AUCs were 0.92 for IPF vs. non-IPF ILD (CI 0.88-0.96, Figure 1B) and 0.82 for fibrosis vs. no fibrosis (CI 0.77-0.86). These findings were confirmed in the validation set. Furthermore, SpiroNose reliably distinguished between IPF and CTD-ILD (AUC 0.98, CI 0.95-1.0), IPF vs. CHP (AUC 0.87, CI 0.77-0.97) and IPF vs. iNSIP (AUC 0.98, CI 0.96-1). Conclusion: Different ILDs have distinct breathprints. Hence, exhaled breath analysis using eNose technology could be a promising new biomarker in ILD and enable timely diagnosis in the future.