Background: Analysis of exhaled breath, especially of volatile organic compounds (VOCs

No abstract

Non-invasive methods with potential for diagnosis of lung diseases gain increasing interest. Within the present study the exhaled breath of 132 persons (97 Chronic obstructive pulmonary disease (COPD) patients [35 COPD without lung cancer, 62 COPD with lung cancer] and 35 healthy volunteers) was investigated using an Ion Mobility Spectrometer (IMS) coupled to a Multi-Capillary Column (MCC) without any pre-separation or pre-enrichment. One hundred four different peaks were considered within the IMSChromatograms of the 10 mL breath samples of both groups. A principal component analysis (PCA) of these 104 peaks identified a single analyte, that allowed a separation of the healthy persons and the COPD patients (with and without lung cancer). The sensitivity obtained was 60%, the specificity 91%, the positive predictive value 95%. The peak was characterized as cyclohexanone (CAS 108-94-1). Subsequent studies must validate the identity of the peak used for separation of the two groups with a greater population and external standards. Breath gas analysis using ion mobility spectrometry offers a chance of separating healthy persons and COPD patients using a single analyte at a defined concentration.

The prevalence of CompSA or persisting CSA in patients with OSA and normal BNP levels who are receiving CPAP therapy is low (1.57%). ASV is an effective treatment for these patients.

Human breath analysis is a powerful and especially a non-invasive technique for the monitoring and hopefully also for the diagnosis of respiratory diseases, including chronic obstructive pulmonary disease (COPD). The exhaled breath of 95 patients suffering COPD and of 35 healthy controls was investigated using an Ion Mobility Spectrometer (IMS) coupled to a Multi-Capillary Column (MCC) without any pre-separation or pre-enrichment. Starting with the results from a Mann-Whitney-Wilcoxon rank sum test to find analytes with the highest potential with respect to differentiation, box and whisker plots, metabolic maps and probability charts were introduced and compared. In addition, the sensitivity, specificity, positive and negative predictive values and the accuracy of the relation were also summarized. The findings were compared to the results of a principal component analysis. Finally, decision trees were introduced to visualize the interdependencies between the analytes and the classifications. The application of these biostatistical methods with simultaneous inclusion of several VOCs for disease classification by ion mobility spectrometry of human breath will provide much more information than using single peaks and single concentration dependencies for disease classification and discrimination of various groups. Towards the future application of potential biomarkers for clinical diagnostic procedures, complex analytical methods, such as ion mobility spectrometry, need statistical and bioinformatical tools which are simple in application, visualize the results and support decisions on the basis of the data obtained from measurements of analytes in exhaled human breath.