Detecting Coronary Artery Disease Using Exhaled Breath Analysis

Abstract

Introduction: Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide, and there is an unmet need for a simple, inexpensive, noninvasive tool aimed at CAD detection. The aim of this pilot study was to evaluate the possible use of breath analysis in detecting the presence of CAD. Materials and Methods: In a prospective study, breath from patients with no history of CAD who presented with acute chest pain to the emergency room was sampled using a designated portable electronic nose (eNose) system. First, breath samples from 60 patients were analyzed and categorized as obstructive, nonobstructive, and no-CAD according to the actual presence and extent of CAD as was demonstrated on cardiac imaging (either computerized tomography angiography or coronary angiography). Classification models were built according to the results, and their diagnostic performance was then examined in a blinded manner on a new set of 25 patients. The data were compared with the actual results of coronary arteries evaluation. Sensitivity, specificity, and accuracy were calculated for each model. Results: Obstructive CAD was correctly distinguished from nonobstructive and no-CAD with 89% sensitivity, 31% specificity, 83% negative predictive value (NPV), 42% positive predictive value (PPV), and 52% accuracy. In another model, any extent of CAD was successfully distinguished from no-CAD with 69% sensitivity, 67% specificity, 54% NPV, 79% PPV, and 68% accuracy. Conclusion: This proof-of-concept study shows that breath analysis has the potential to be used as a novel rapid, noninvasive diagnostic tool to help identify presence of CAD in patients with acute chest pain.