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      Identification of recent exacerbations in COPD patients by electronic nose

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          Abstract

          Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months.

          Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA).

          Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1–4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)–area under the curve (AUC)=0.98, 95% CI 0.97–1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94–1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results.

          Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.

          Abstract

          Exhaled breath analysis by eNose can identify COPD patients who recently exacerbated with high accuracy. This suggests that these patients have their own exhaled molecular fingerprint that could be valuable for monitoring purposes. https://bit.ly/34vTyrH

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          Most cited references33

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          pROC: an open-source package for R and S+ to analyze and compare ROC curves

          Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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            Building Predictive Models inRUsing thecaretPackage

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              The Application of Electronic Computers to Factor Analysis

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                Author and article information

                Journal
                ERJ Open Res
                ERJ Open Res
                ERJOR
                erjor
                ERJ Open Research
                European Respiratory Society
                2312-0541
                October 2020
                21 December 2020
                : 6
                : 4
                : 00307-2020
                Affiliations
                [1 ]Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
                [2 ]Breathomix BV, Leiden, The Netherlands
                [3 ]Amsterdam UMC, Vrije Universiteit Amsterdam, Dept of Pediatric Respiratory Medicine, Amsterdam, The Netherlands
                [4 ]Medisch Spectrum Twente, Dept of Pulmonary Function, Enschede, The Netherlands
                [5 ]Medisch Centrum Den Bosch Oost, ’s-Hertogenbosch, The Netherlands
                [6 ]Diagnostiek voor U, Eindhoven, The Netherlands
                [7 ]Franciscus Gasthuis and Vlietland/Erasmus MC, Dept of Pulmonology, Rotterdam, The Netherlands
                [8 ]For a list of the members of the Amsterdam UMC Breath Research Group, see the Acknowledgements section
                Author notes
                Anke H. Maitland-van der Zee, Amsterdam UMC, location AMC, Dept of Respiratory Medicine, room F5-257, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. E-mail: a.h.maitland@ 123456amsterdamumc.nl
                Author information
                https://orcid.org/0000-0002-7133-955X
                Article
                00307-2020
                10.1183/23120541.00307-2020
                7792783
                bc217c22-3a46-49c4-aa23-4e69e23d9490
                Copyright ©ERS 2020

                This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0.

                History
                : 25 May 2020
                : 28 September 2020
                Funding
                Funded by: Boehringer Ingelheim, open-funder-registry 10.13039/100001003;
                Award ID: Unrestricted Grant
                Categories
                Original Articles
                COPD
                1

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