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      Left Ventricular Hypertrophy After Renal Transplantation: Systematic Review and Meta-analysis

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          Abstract

          Background.

          Left ventricular hypertrophy (LVH) in patients with end stage renal disease undergoing renal replacement is linked to an increased risk for cardiovascular diseases. Dialysis does not completely prevent or correct this abnormality, and the evidence for kidney transplantation (KT) varies. This analysis aims to explore the relationship between KT and LVH.

          Methods.

          MEDLINE and Scopus were systematically searched in October 2023. All cross-sectional and longitudinal studies that fulfilled our inclusion criteria were included. Outcome was left ventricular mass index (LVMI) changes. We conducted a meta-analysis using a random effects model. Meta-regression was applied to examine the LVMI changes dependent on various covariates. Sensitivity analysis was used to handle outlying or influential studies and address publication bias.

          Results.

          From 7416 records, 46 studies met the inclusion criteria with 4122 included participants in total. Longitudinal studies demonstrated an improvement of LVMI after KT −0.44 g/m 2 (−0.60 to −0.28). Blood pressure was identified as a predictor of LVMI change. A younger age at the time of KT and well-controlled anemia were also associated with regression of LVH. In studies longitudinally comparing patients on dialysis and renal transplant recipients, no difference was detected −0.09 g/m 2 (−0.33 to 0.16). Meta-regression using changes of systolic blood pressure as a covariate showed an association between higher blood pressure and an increase in LVMI, regardless of the modality of renal replacement treatment.

          Conclusions.

          In conclusion, our results indicated a potential cardiovascular benefit, defined as the regression of LVH, after KT. This benefit was primarily attributed to improved blood pressure control rather than the transplantation itself.

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          Most cited references81

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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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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              Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis.

              We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome. These are simple rank-based data augmentation techniques, which formalize the use of funnel plots. We show that they provide effective and relatively powerful tests for evaluating the existence of such publication bias. After adjusting for missing studies, we find that the point estimate of the overall effect size is approximately correct and coverage of the effect size confidence intervals is substantially improved, in many cases recovering the nominal confidence levels entirely. We illustrate the trim and fill method on existing meta-analyses of studies in clinical trials and psychometrics.
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                Author and article information

                Contributors
                Journal
                Transplant Direct
                Transplant Direct
                TXD
                Transplantation Direct
                Lippincott Williams & Wilkins (Hagerstown, MD )
                2373-8731
                17 May 2024
                June 2024
                : 10
                : 6
                : e1647
                Affiliations
                [1 ] Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany.
                [2 ] Department of Pediatric Kidney, Liver and Metabolic Diseases, Hannover Medical School, Hannover, Germany.
                Author notes
                Correspondence: Bernhard M.W. Schmidt, MD, SM, Department of Nephrology and Hypertension, Hannover Medical School, Germany, Carl-Neuberg-Straße 1, 30625 Hannover, Germany. ( schmidt.bernhard@ 123456mh-hannover.de ).
                Author information
                https://orcid.org/0000-0002-7700-7142
                https://orcid.org/0000-0002-8164-6318
                Article
                TXD-2024-0075 00024
                10.1097/TXD.0000000000001647
                11104731
                38769973
                96298017-3d8a-4a3c-af99-0a5256a6169a
                Copyright © 2024 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 March 2024
                : 14 March 2024
                Categories
                Kidney Transplantation
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