OBJECTIVES/HYPOTHESIS: The electronic nose is a sensor of volatile molecules that is useful in the analysis of expired gases. The device is well suited to testing the breath of patients receiving mechanical ventilation and is a potential diagnostic adjunct that can aid in the detection of patients with ventilator-associated pneumonia. STUDY DESIGN: A prospective study. METHODS: We performed a prospective study of patients receiving mechanical ventilation in a surgical intensive care unit who underwent chest computed tomography (CT) scanning. A single attending radiologist reviewed the chest CT scans, and imaging features were recorded on a standardized form. Within 48 hours of chest CT scan, five sets of exhaled gas were sampled from the expiratory limb of the ventilator circuit. The gases were assayed with a commercially available electronic nose. Both linear and nonlinear analyses were performed to identify correlations between imaging features and the assayed gas signatures. RESULTS: Twenty-five patients were identified, 13 of whom were diagnosed with pneumonia by CT scan. Support vector machine analysis was performed in two separate analyses. In the first analysis, in which a training set was identical to a prediction set, the accuracy of prediction results was greater than 91.6%. In the second analysis, in which the training set and the prediction set were different, the accuracy of prediction results was at least 80%, with higher accuracy depending on the specific parameters and models being used. CONCLUSION: The electronic nose is a new technology that continues to show promise as a potential diagnostic adjunct in the diagnosis of pneumonia and other infectious diseases.
OBJECTIVES/HYPOTHESIS: The electronic nose is a sensor of volatile molecules that is useful in the analysis of expired gases. The device is well suited to testing the breath of patients receiving mechanical ventilation and is a potential diagnostic adjunct that can aid in the detection of patients with ventilator-associated pneumonia. STUDY DESIGN: A prospective study. METHODS: We performed a prospective study of patients receiving mechanical ventilation in a surgical intensive care unit who underwent chest computed tomography (CT) scanning. A single attending radiologist reviewed the chest CT scans, and imaging features were recorded on a standardized form. Within 48 hours of chest CT scan, five sets of exhaled gas were sampled from the expiratory limb of the ventilator circuit. The gases were assayed with a commercially available electronic nose. Both linear and nonlinear analyses were performed to identify correlations between imaging features and the assayed gas signatures. RESULTS: Twenty-five patients were identified, 13 of whom were diagnosed with pneumonia by CT scan. Support vector machine analysis was performed in two separate analyses. In the first analysis, in which a training set was identical to a prediction set, the accuracy of prediction results was greater than 91.6%. In the second analysis, in which the training set and the prediction set were different, the accuracy of prediction results was at least 80%, with higher accuracy depending on the specific parameters and models being used. CONCLUSION: The electronic nose is a new technology that continues to show promise as a potential diagnostic adjunct in the diagnosis of pneumonia and other infectious diseases.
Authors: Matteo Bassetti; Garyphallia Poulakou; Etienne Ruppe; Emilio Bouza; Sebastian J Van Hal; Adrian Brink Journal: Intensive Care Med Date: 2017-07-21 Impact factor: 17.440
Authors: Koen de Heer; Marc P van der Schee; Koos Zwinderman; Inge A H van den Berk; Caroline Elisabeth Visser; Rien van Oers; Peter J Sterk Journal: J Clin Microbiol Date: 2013-03-06 Impact factor: 5.948
Authors: Jussi Virtanen; Lauri Hokkinen; Markus Karjalainen; Anton Kontunen; Risto Vuento; Jura Numminen; Markus Rautiainen; Niku Oksala; Antti Roine; Ilkka Kivekäs Journal: Eur Arch Otorhinolaryngol Date: 2018-07-24 Impact factor: 2.503