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      Silent brain infarcts and early cognitive outcomes after transcatheter aortic valve implantation: a systematic review and meta-analysis

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          Abstract

          Background

          Silent brain infarcts (SBIs) are frequently identified after transcatheter aortic valve implantation (TAVI), when patients are screened with diffusion-weighted magnetic resonance imaging (DW-MRI). Outside the cardiac literature, SBIs have been correlated with progressive cognitive dysfunction; however, their prognostic utility after TAVI remains uncertain. This study’s main goals were to explore (i) the incidence of and potential risk factors for SBI after TAVI; and (ii) the effect of SBI on early post-procedural cognitive dysfunction (PCD).

          Methods and results

          A systematic literature review was performed to identify all publications reporting SBI incidence, as detected by DW-MRI after TAVI. Silent brain infarct incidence, baseline characteristics, and the incidence of early PCD were evaluated via meta-analysis and meta-regression models. We identified 39 relevant studies encapsulating 2408 patients. Out of 2171 patients who underwent post-procedural DW-MRI, 1601 were found to have at least one new SBI (pooled effect size 0.76, 95% CI: 0.72–0.81). The incidence of reported stroke with focal neurological deficits was 3%. Meta-regression noted that diabetes, chronic renal disease, 3-Tesla MRI, and pre-dilation were associated with increased SBI risk. The prevalence of early PCD increased during follow-up, from 16% at 10.0 ± 6.3 days to 26% at 6.1 ± 1.7 months and meta-regression suggested an association between the mean number of new SBI and incidence of PCD. The use of cerebral embolic protection devices (CEPDs) appeared to decrease the volume of SBI, but not their overall incidence.

          Conclusions

          Silent brain infarcts are common after TAVI; and diabetes, kidney disease, and pre-dilation increase overall SBI risk. While higher numbers of new SBIs appear to adversely affect early neurocognitive outcomes, long-term follow-up studies remain necessary as TAVI expands to low-risk patient populations. The use of CEPD did not result in a significant decrease in the occurrence of SBI.

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          Most cited references51

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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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              Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range

              The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its importance, is either ignored or used in a stepwise but somewhat arbitrary manner, e.g. the famous method proposed by Hozo et al. We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. The real data application indicates that our proposed estimators are capable to serve as "rules of thumb" and will be widely applied in evidence-based medicine.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                European Heart Journal
                Oxford University Press (OUP)
                0195-668X
                1522-9645
                February 01 2021
                February 01 2021
                Affiliations
                [1 ]Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
                [2 ]Cardiothoracic Surgical Department, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
                [3 ]Baird Institute of Applied Heart and Lung Research, 100 Carillon Avenue, Sydney, NSW 2042, Australia
                [4 ]The Prince Charles Hospital, Critical Care Research Group, Brisbane, QLC 4032, Australia
                [5 ]Faculty of Medicine, University of Queensland, Brisbane, QLD 4072, Australia
                [6 ]Sydney Translational Imaging Laboratory, Charles Perkins Centre, University of Sydney, NSW 2006, Australia
                [7 ]Department of Radiology, Royal Prince Alfred Hospital, Camperdown, Sydney, NSW 2050, Australia
                Article
                10.1093/eurheartj/ehab002
                33517376
                9463de5f-6d17-4a2d-b3b7-fd71a8992fe2
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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