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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.

Background Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening. Methods A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant analysis (LDA) was used as a classifying model of the sensing signals obatined from the examined groups. For the testing step, an additional device was added. The study group included 274 patients: 94 with gastric cancer, 67 who were in the high-risk group, and 113 controls. Results The results of the test set showed a clear discrimination between patients with gastric cancer and controls using the 2-device LDA model (area under the curve, 93.8%; sensitivity, 100%; specificity, 87.5%; overall accuracy, 91.1%), and acceptable results were also achieved for patients with high-risk lesions (the corresponding values for dysplasia were 84.9%, 45.2%, 87.5%, and 65.9%, respectively). The test-phase analysis showed lower accuracies, though still clinically useful. Conclusion Our results demonstrate that a portable breath sensor device could be useful in point-of-care settings. It shows a promise for detection of gastric cancer as well as for other types of disease. Lay summary A portable sensor-based breath analyzer for detection of gastric cancer can be used in point-of-care settings. The results are transferrable between devices via advanced IoT technology. Both the hardware and software of the reported breath analyzer could be easily modified to enable detection and monitirng of other disease states.