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      Clinical laboratory parameters and fatality of Severe fever with thrombocytopenia syndrome patients: A systematic review and meta-analysis

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          Abstract

          Background

          Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease with high case fatality rate. Unfortunately, no vaccine or antiviral specifically targeting SFTS virus (SFTSV) are available for the time being. Our objective was to investigate the association between clinical laboratory parameters and fatality of SFTS patients.

          Methods

          The systematic review was conducted in accordance with The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. We searched (from inception to 24th February 2022) Web of Science, PubMed, National Knowledge Infrastructure databases and Wan Fang Data for relevant researchers on SFTS. Studies were eligible if they reported on laboratory parameters of SFTS patients and were stratified by clinical outcomes. A modified version of Newcastle-Ottawa scale was used to evaluate the quality of included studies. Standardized mean difference (SMD) was used to evaluate the association between laboratory parameters and outcomes. The between-study heterogeneity was evaluated quantitatively by standard Chi-square and the index of heterogeneity ( I 2). Heterogeneity was explored by subgroup and sensitivity analyses, and univariable meta-regression. Publication bias was determined using funnel plots and Egger’s test.

          Results

          We identified 34 relevant studies, with over 3300 participants across three countries. The following factors were strongly (SMD>1 or SMD<-0.5) and significantly ( P<0.05) associated mortality: thrombin time (TT) (SMD = 1.53), viral load (SMD = 1.47), activated partial-thromboplastin time (APTT) (SMD = 1.37), aspartate aminotransferase (AST) (SMD = 1.19), lactate dehydrogenase (LDH) (SMD = 1.13), platelet count (PLT) (SMD = -0.47), monocyte percentage (MON%) (SMD = -0.47), lymphocyte percentage (LYM%) (SMD = -0.46) and albumin (ALB) (SMD = -0.43). Alanine aminotransferase, AST, creatin phosphokinase, LDH, PLT, partial-thromboplastin time and viral load contributed to the risk of dying of SFTS patients in each subgroup analyses. Sensitivity analysis demonstrated that the results above were robust.

          Conclusions/significance

          The abnormal levels of viral load, PLT, coagulation function and liver function, significantly increase the risk of SFTS mortality, suggesting that SFTS patients with above symptoms call for special concern.

          Author summary

          Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high case-fatality rate and lack of vaccines, calling for an urgent need to identify the risk factors of mortality. Compared to the previous studies concentrating on clinical manifestations diagnosed partly relying on empirical subjective assessment, our study aimed to systematically analyzed the association between SFTS patients’ outcomes and clinical laboratory parameters. What’s more, no consistent conclusion derived because of sample sizes with enormous differences. In this systematic review, we searched the medical literature and found 34 studies evaluating associations between laboratory factors and risk of dying among SFTS patients. These studies described 3388 SFTS patients of whom 739 (21.81%) died. SFTS patients were at increased risk of dying if they had abnormal strongly levels of viral load, PLT, coagulation function and liver function. Therefore, the patients with above-mentioned situations should be monitored and cured carefully.

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          Most cited references60

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          Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

          Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
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            Estimating the mean and variance from the median, range, and the size of a sample

            Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.
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              Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness.

              Systematic review aims to systematically identify, critically appraise, and summarize all relevant studies that match predefined criteria and answer predefined questions. The most common type of systematic review is that assessing the effectiveness of an intervention or therapy. In this article, we discuss some of the common methodological issues that arise when conducting systematic reviews and meta-analyses of effectiveness data, including issues related to study designs, meta-analysis, and the use and interpretation of effect sizes.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                17 June 2022
                June 2022
                : 16
                : 6
                : e0010489
                Affiliations
                [1 ] Department of Microbiological Laboratory Technology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
                [2 ] National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
                NIAID Integrated Research Facility, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-5694-0021
                Article
                PNTD-D-21-01731
                10.1371/journal.pntd.0010489
                9246219
                35714138
                932a828e-f167-44ff-9640-231fdd2fb313
                © 2022 Wang 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
                : 9 December 2021
                : 10 May 2022
                Page count
                Figures: 1, Tables: 1, Pages: 16
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                People and Places
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                Asia
                China
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
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                Viral Load
                Research and Analysis Methods
                Research Assessment
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                Biology and Life Sciences
                Biochemistry
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                Enzymes
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                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Creatinine
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-06-30
                All relevant data are within the manuscript and its Supporting information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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