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      The Effectiveness of Noninvasive Biomarkers to Predict Hepatitis B-Related Significant Fibrosis and Cirrhosis: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

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          Abstract

          Noninvasive biomarkers have been developed to predict hepatitis B virus (HBV)-related fibrosis owing to the significant limitations of liver biopsy. Those biomarkers were initially derived from evaluation of hepatitis C virus (HCV)-related fibrosis, and their accuracy among HBV-infected patients was under constant debate. A systematic review was conducted on records in PubMed, EMBASE and the Cochrane Library electronic databases, up until April 1st, 2013, in order to systematically assess the effectiveness and accuracy of these biomarkers for predicting HBV-related fibrosis. The questionnaire for quality assessment of diagnostic accuracy studies (QUADAS) was used. Out of 115 articles evaluated for eligibility, 79 studies satisfied the pre-determined inclusion criteria for meta-analysis. Eventually, our final data set for the meta-analysis contained 30 studies. The areas under the SROC curve for APRI, FIB-4, and FibroTest of significant fibrosis were 0.77, 0.75, and 0.84, respectively. For cirrhosis, the areas under the SROC curve for APRI, FIB-4 and FibroTest were 0.75, 0.87, and 0.90, respectively. The heterogeneity of FIB-4 and FibroTest were not statistically significant. The heterogeneity of APRI for detecting significant fibrosis was affected by median age ( P = 0.0211), and for cirrhosis was affected by etiology ( P = 0.0159). Based on the analysis we claim that FibroTest has excellent diagnostic accuracy for identification of HBV-related significant fibrosis and cirrhosis. FIB-4 has modest benefits and may be suitable for wider scope implementation.

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          Histological grading and staging of chronic hepatitis.

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            Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations.

            We consider how to combine several independent studies of the same diagnostic test, where each study reports an estimated false positive rate (FPR) and an estimated true positive rate (TPR). We propose constructing a summary receiver operating characteristic (ROC) curve by the following steps. (i) Convert each FPR to its logistic transform U and each TPR to its logistic transform V after increasing each observed frequency by adding 1/2. (ii) For each study calculate D = V - U, which is the log odds ratio of TPR and FPR, and S = V + U, an implied function of test threshold; then plot each study's point (Si, Di). (iii) Fit a robust-resistant regression line to these points (or an equally weighted least-squares regression line), with V - U as the dependent variable. (iv) Back-transform the line to ROC space. To avoid model-dependent extrapolation from irrelevant regions of ROC space we propose defining a priori a value of FPR so large that the test simply would not be used at that FPR, and a value of TPR so low that the test would not be used at that TPR. Then (a) only data points lying in the thus defined north-west rectangle of the unit square are used in the data analysis, and (b) the estimated summary ROC is depicted only within that subregion of the unit square. We illustrate the methods using simulated and real data sets, and we point to ways of comparing different tests and of taking into account the effects of covariates.
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              Classification of chronic viral hepatitis: a need for reassessment.

              P Scheuer (1991)
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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, USA )
                1932-6203
                2014
                25 June 2014
                : 9
                : 6
                : e100182
                Affiliations
                [1 ]School of Public Health, China Medical University, Shenyang, PR China
                [2 ]Department of Clinical Laboratory, Shengjing Hospital of China Medical University, Shenyang, PR China
                [3 ]Department of Urinary Surgery, Changhai Hospital, Shanghai, PR China
                Drexel University College of Medicine, United States of America
                Author notes

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

                Conceived and designed the experiments: HBL XYX. Analyzed the data: XYX WSA XFW HBL. Wrote the paper: HBL XYX HK RXS YHZ XFW WSA. Data collection: XYX HK RXS YHZ. First draft of the manuscript: XYX HBL.

                Article
                PONE-D-13-46061
                10.1371/journal.pone.0100182
                4070977
                24964038
                13733002-0e7c-4d83-9fe8-d63ef672c787
                Copyright @ 2014

                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
                : 3 December 2013
                : 22 May 2014
                Page count
                Pages: 16
                Funding
                This research was supported by a National Natural Science Foundation Grant in China (No. 71073176) awarded to H.B.L. 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
                Diagnostic Medicine
                Gastroenterology and hepatology
                Liver diseases
                Infectious hepatitis
                Hepatitis B
                Cirrhosis
                Public and Occupational Health
                Global Health
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Meta-Analysis
                Research and Analysis Methods
                Research Assessment
                Systematic Reviews
                Research Design
                Clinical Research Design

                Uncategorized
                Uncategorized

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