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      A Systematic Review of Diagnostic Biomarkers of COPD Exacerbation

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          Abstract

          The aims of this systematic review were to determine which blood-based molecules have been evaluated as possible biomarkers to diagnose chronic obstructive pulmonary disease (COPD) exacerbations (AECOPD) and to ascertain the quality of these biomarker publications. Patients of interest were those that have been diagnosed with COPD. MEDLINE, EMBASE, and CINAHL databases were searched systematically through February 2015 for publications relating to AECOPD diagnostic biomarkers. We used a modified guideline for the REporting of tumor MARKer Studies (mREMARK) to assess study quality. Additional components of quality included the reporting of findings in a replication cohort and the use of receiver-operating characteristics area-under-the curve statistics in evaluating performance. 59 studies were included, in which the most studied biomarkers were C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). CRP showed consistent elevations in AECOPD compared to control subjects, while IL-6 and TNF-α had variable statistical significance and results. mREMARK scores ranged from 6 to 18 (median score of 13). 12 articles reported ROC analyses and only one study employed a replication cohort to confirm biomarker performance. Studies of AECOPD diagnostic biomarkers remain inconsistent in their reporting, with few studies employing ROC analyses and even fewer demonstrating replication in independent cohorts.

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Understanding diagnostic tests 3: Receiver operating characteristic curves.

            The results of many clinical tests are quantitative and are provided on a continuous scale. To help decide the presence or absence of disease, a cut-off point for 'normal' or 'abnormal' is chosen. The sensitivity and specificity of a test vary according to the level that is chosen as the cut-off point. The receiver operating characteristic (ROC) curve, a graphical technique for describing and comparing the accuracy of diagnostic tests, is obtained by plotting the sensitivity of a test on the y axis against 1-specificity on the x axis. Two methods commonly used to establish the optimal cut-off point include the point on the ROC curve closest to (0, 1) and the Youden index. The area under the ROC curve provides a measure of the overall performance of a diagnostic test. In this paper, the author explains how the ROC curve can be used to select optimal cut-off points for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of tests. The ROC curve is obtained by calculating the sensitivity and specificity of a test at every possible cut-off point, and plotting sensitivity against 1-specificity. The curve may be used to select optimal cut-off values for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of different tests.
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              Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis.

              Individuals with chronic obstructive pulmonary disease (COPD) are at increased risk of cardiovascular diseases, osteoporosis, and muscle wasting. Systemic inflammation may be involved in the pathogenesis of these disorders. A study was undertaken to determine whether systemic inflammation is present in stable COPD. A systematic review was conducted of studies which reported on the relationship between COPD, forced expiratory volume in 1 second (FEV(1)) or forced vital capacity (FVC), and levels of various systemic inflammatory markers: C-reactive protein (CRP), fibrinogen, leucocytes, tumour necrosis factor-alpha (TNF-alpha), and interleukins 6 and 8. Where possible the results were pooled together to produce a summary estimate using a random or fixed effects model. Fourteen original studies were identified. Overall, the standardised mean difference in the CRP level between COPD and control subjects was 0.53 units (95% confidence interval (CI) 0.34 to 0.72). The standardised mean difference in the fibrinogen level was 0.47 units (95% CI 0.29 to 0.65). Circulating leucocytes were also higher in COPD than in control subjects (standardised mean difference 0.44 units (95% CI 0.20 to 0.67)), as were serum TNF-alpha levels (standardised mean difference 0.59 units (95% CI 0.29 to 0.89)). Reduced lung function is associated with increased levels of systemic inflammatory markers which may have important pathophysiological and therapeutic implications for subjects with stable COPD.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 July 2016
                2016
                : 11
                : 7
                : e0158843
                Affiliations
                [001]Centre for Heart Lung Innovation, Institute for Heart Lung Health at St. Paul’s Hospital & Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada
                Helmholtz Zentrum München, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: YWRC DDS. Performed the experiments: YWRC JML. Analyzed the data: YWRC JML DDS. Contributed reagents/materials/analysis tools: YWRC JML DDS. Wrote the paper: YWRC JML.

                Article
                PONE-D-16-13853
                10.1371/journal.pone.0158843
                4951145
                27434033
                6f90eacd-d6ee-483f-9a9b-04d1d472572b
                © 2016 Chen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 April 2016
                : 22 June 2016
                Page count
                Figures: 1, Tables: 2, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008762, Genome Canada;
                Award Recipient :
                Funding was provided by Genome Canada ( www.genomecanada.ca), grant title "Clinical Implementation and Outcomes Evaluation of Blood-Based Biomarkers for COPD Management", recipient DDS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Medicine and Health Sciences
                Pulmonology
                Chronic Obstructive Pulmonary Disease
                Biology and Life Sciences
                Physiology
                Immune Physiology
                Antibodies
                Medicine and Health Sciences
                Physiology
                Immune Physiology
                Antibodies
                Biology and Life Sciences
                Immunology
                Immune System Proteins
                Antibodies
                Medicine and Health Sciences
                Immunology
                Immune System Proteins
                Antibodies
                Biology and Life Sciences
                Biochemistry
                Proteins
                Immune System Proteins
                Antibodies
                Biology and Life Sciences
                Biochemistry
                Glycobiology
                Glycoproteins
                Fibrinogen
                People and Places
                Geographical Locations
                Africa
                Egypt
                Research and Analysis Methods
                Research Assessment
                Systematic Reviews
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                Eosinophils
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                Eosinophils
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Eosinophils
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Eosinophils
                Research and Analysis Methods
                Database and Informatics Methods
                Database Searching
                Custom metadata
                All data tables are provided in the paper.

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