Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank

Abstract

Background: There is an increasing interest in employing electronic nose technology in the diagnosis and monitoring of lung diseases. Interstitial lung diseases (ILD) are challenging in regard to setting an accurate diagnosis in a timely manner. Thus, there is a high unmet need in non-invasive diagnostic tests. This single-center explorative study aimed to evaluate the usefulness of electronic nose (Aeonose®) in the diagnosis of ILDs. Methods: Exhaled volatile organic compound (VOC) signatures were obtained by Aeonose® in 174 ILD patients, 23 patients with chronic obstructive pulmonary disease (COPD), and 33 healthy controls (HC). Results: By dichotomous comparison of VOC’s between ILD, COPD, and HC, a discriminating algorithm was established. In addition, direct analyses between the ILD subgroups, e.g., cryptogenic organizing pneumonia (COP, n = 28), idiopathic pulmonary fibrosis (IPF, n = 51), and connective tissue disease-associated ILD (CTD-ILD, n = 25) were performed. Area under the Curve (AUC) and Matthews’s correlation coefficient (MCC) were used to interpret the data. In direct comparison of the different ILD subgroups to HC, the algorithms developed on the basis of the Aeonose® signatures allowed safe separation between IPF vs. HC (AUC of 0.95, MCC of 0.73), COP vs. HC (AUC 0.89, MCC 0.67), and CTD-ILD vs. HC (AUC 0.90, MCC 0.69). Additionally, to a case-control study design, the breath patterns of ILD subgroups were compared to each other. Following this approach, the sensitivity and specificity showed a relevant drop, which results in a poorer performance of the algorithm to separate the different ILD subgroups (IPF vs. COP with MCC 0.49, IPF vs. CTD-ILD with MCC 0.55, and COP vs. CT-ILD with MCC 0.40). Conclusions: The Aeonose® showed some potential in separating ILD subgroups from HC. Unfortunately, when applying the algorithm to distinguish ILD subgroups from each other, the device showed low specificity. We suggest that artificial intelligence or principle compound analysis-based studies of a much broader data set of patients with ILDs may be much better suited to train these devices.

Assessing the Effectiveness of Pirfenidone in Idiopathic Pulmonary Fibrosis: Long-Term, Real-World Data from European IPF Registry (eurIPFreg)

“…Greater impairment in forced vital capacity (FVC) or diffusion capacity of the lungs for carbon monoxide (DLco), and a greater extent of fibrotic changes on HRCT, are known mortality predictors in ILD patients [ 11 ]. Despite substantial advancements in the understanding of the pathogenesis of IPF having been achieved, the course, progression factors, biomarkers and the response to the treatment of an individual patient still cannot be reliably predicted [ 12 , 13 , 14 , 15 ].…”

Nosocomial Pneumonia in the Era of Multidrug-Resistance: Updates in Diagnosis and Management

“…VOCs attach to the sensor polymer surface and induce swelling of the polymer film, increasing the electrical resistance and generating an electrical signal [ 95 ]. These signals can be classified into VOC signatures using algorithms and a database of previously recorded VOC patterns [ 94 , 96 ]. Distinct VOC patterns have the potential to serve as markers of inflammatory, microbial, oxidative and neoplastic conditions [ 96 , 97 , 98 ].…”

“…These signals can be classified into VOC signatures using algorithms and a database of previously recorded VOC patterns [ 94 , 96 ]. Distinct VOC patterns have the potential to serve as markers of inflammatory, microbial, oxidative and neoplastic conditions [ 96 , 97 , 98 ]. The eNose technology has been implemented in the food and beverage industry to monitor air quality and to detect explosive and chemical agents in the environment [ 96 , 99 ].…”

“…Distinct VOC patterns have the potential to serve as markers of inflammatory, microbial, oxidative and neoplastic conditions [ 96 , 97 , 98 ]. The eNose technology has been implemented in the food and beverage industry to monitor air quality and to detect explosive and chemical agents in the environment [ 96 , 99 ]. In COPD subjects, the eNose was able to distinguish between those with a viral or bacterial infection and those without an infection [ 100 ].…”

Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection

“…With this, new areas of research may evolve. As an example, recent work has demonstrated the ability of the eNose to diagnose different types of interstitial lung diseases (including cryptogenic organizing pneumonia, idiopathic pulmonary fibrosis, and connective tissue disease-associated ILD) [ 79 , 80 ]. eNoses are also becoming increasingly utilized to detect biomarkers of various types of malignancies outside of the respiratory system, including colorectal cancer, and Barrett’s esophagus, the precursor to esophageal adenocarcinoma [ 81 , 82 ].…”