Literature DB >> 26440675

Electronic nose analysis of exhaled breath to diagnose ventilator-associated pneumonia.

R M Schnabel1, M L L Boumans2, A Smolinska3, E E Stobberingh2, R Kaufmann4, P M H J Roekaerts4, D C J J Bergmans4.   

Abstract

BACKGROUND: Exhaled breath analysis is an emerging technology in respiratory disease and infection. Electronic nose devices (e-nose) are small and portable with a potential for point of care application. Ventilator-associated pneumonia (VAP) is a common nosocomial infection occurring in the intensive care unit (ICU). The current best diagnostic approach is based on clinical criteria combined with bronchoalveolar lavage (BAL) and subsequent bacterial culture analysis. BAL is invasive, laborious and time consuming. Exhaled breath analysis by e-nose is non-invasive, easy to perform and could reduce diagnostic time. Aim of this study was to explore whether an e-nose can be used as a non-invasive in vivo diagnostic tool for VAP.
METHODS: Seventy-two patients met the clinical diagnostic criteria of VAP and underwent BAL. In thirty-three patients BAL analysis confirmed the diagnosis of VAP [BAL+(VAP+)], in thirty-nine patients the diagnosis was rejected [BAL-]. Before BAL was performed, exhaled breath was sampled from the expiratory limb of the ventilator into sterile Tedlar bags and subsequently analysed by an e-nose with metal oxide sensors (DiagNose, C-it, Zutphen, The Netherlands). From further fifty-three patients without clinical suspicion of VAP or signs of respiratory disease exhaled breath was collected to serve as a control group [control(VAP-]). The e-nose data from exhaled breath were analysed using logistic regression.
RESULTS: The ROC curve comparing [BAL+(VAP+)] and [control(VAP-)] patients had an area under the curve (AUC) of 0.82 (95% CI 0.73-0.9). The sensitivity was 88% with a specificity of 66%. The comparison of [BAL+(VAP+)] and [BAL-] patients revealed an AUC of 0.69; 95% CI 0.57-0.81) with a sensitivity of 76% with a specificity of 56%.
CONCLUSION: E-nose lacked sensitivity and specificity in the diagnosis of VAP in the present study for current clinical application. Further investigation into this field is warranted to explore the diagnostic possibilities of this promising new technique.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Bronchoalveolar lavage; Critical care; Electronic nose; Pneumonia

Mesh:

Year:  2015        PMID: 26440675     DOI: 10.1016/j.rmed.2015.09.014

Source DB:  PubMed          Journal:  Respir Med        ISSN: 0954-6111            Impact factor:   3.415


  22 in total

Review 1.  Electronic Nose Technology in Respiratory Diseases.

Authors:  Silvano Dragonieri; Giorgio Pennazza; Pierluigi Carratu; Onofrio Resta
Journal:  Lung       Date:  2017-02-25       Impact factor: 2.584

Review 2.  Antimicrobial resistance in the next 30 years, humankind, bugs and drugs: a visionary approach.

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

3.  Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation.

Authors:  Luis F Valdez; Juan Manuel Gutiérrez
Journal:  Sensors (Basel)       Date:  2016-10-20       Impact factor: 3.576

4.  Factors Influencing Continuous Breath Signal in Intubated and Mechanically-Ventilated Intensive Care Unit Patients Measured by an Electronic Nose.

Authors:  Jan Hendrik Leopold; Ameen Abu-Hanna; Camilla Colombo; Peter J Sterk; Marcus J Schultz; Lieuwe D J Bos
Journal:  Sensors (Basel)       Date:  2016-08-22       Impact factor: 3.576

5.  BreathDx - molecular analysis of exhaled breath as a diagnostic test for ventilator-associated pneumonia: protocol for a European multicentre observational study.

Authors:  Pouline M P van Oort; Tamara Nijsen; Hans Weda; Hugo Knobel; Paul Dark; Timothy Felton; Nicholas J W Rattray; Oluwasola Lawal; Waqar Ahmed; Craig Portsmouth; Peter J Sterk; Marcus J Schultz; Tetyana Zakharkina; Antonio Artigas; Pedro Povoa; Ignacio Martin-Loeches; Stephen J Fowler; Lieuwe D J Bos
Journal:  BMC Pulm Med       Date:  2017-01-03       Impact factor: 3.317

6.  Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models.

Authors:  Bartosz Szulczyński; Piotr Rybarczyk; Jacek Gębicki
Journal:  Monatsh Chem       Date:  2018-08-10       Impact factor: 1.451

7.  Rapid assessment of the authenticity of limequat fruit using the electronic nose and gas chromatography coupled with mass spectrometry.

Authors:  Martyna Lubinska-Szczygeł; Dominika Pudlak; Tomasz Dymerski; Jacek Namieśnik
Journal:  Monatsh Chem       Date:  2018-08-09       Impact factor: 1.451

8.  Predicting ventilator-associated pneumonia with machine learning.

Authors:  Christine Giang; Jacob Calvert; Keyvan Rahmani; Gina Barnes; Anna Siefkas; Abigail Green-Saxena; Jana Hoffman; Qingqing Mao; Ritankar Das
Journal:  Medicine (Baltimore)       Date:  2021-06-11       Impact factor: 1.817

9.  Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model.

Authors:  Chao Peng; Jia Yan; Shukai Duan; Lidan Wang; Pengfei Jia; Songlin Zhang
Journal:  Sensors (Basel)       Date:  2016-04-11       Impact factor: 3.576

Review 10.  Study on Interference Suppression Algorithms for Electronic Noses: A Review.

Authors:  Zhifang Liang; Fengchun Tian; Simon X Yang; Ci Zhang; Hao Sun; Tao Liu
Journal:  Sensors (Basel)       Date:  2018-04-12       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.