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      Different dosing regimens of Tenecteplase in acute ischemic stroke: A network meta-analysis of the clinical evidence

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          Abstract

          Introduction:

          Acute ischemic stroke remains the major cause of death and disability and conclusive evidence of Tenecteplase in treating stroke is lacking.

          Objective:

          To conduct a meta-analysis to determine whether Tenecteplase produces better outcomes than Alteplase and a network meta-analysis comparing the different dosing regimens of Tenecteplase.

          Methods:

          Searches were made in MEDLINE, CENTRAL, and ClinicalTrials.gov. The outcome measures are recanalization, early neurological improvement, functional outcomes at 90 days (modified Rankin Scale 0–1 and 0–2), intracranial hemorrhage, symptomatic intracranial hemorrhage, and mortality within 90 days from treatment.

          Results:

          Fourteen studies are included in the meta-analyses and 18 studies in the network meta-analyses. In the meta-analysis, Tenecteplase 0.25 mg/kg has significant results in early neurological improvement (OR = 2.35, and 95% CI = 1.16–4.72) and excellent functional outcome (OR = 1.20, and 95% CI = 1.02–1.42). In the network meta-analysis, Tenecteplase 0.25 mg/kg produces significant results in early neurological improvement (OR = 1.52 [95% CI = 1.13–2.05], p-value = 0.01), functional outcomes (mRS 0–1 and 0–2) (OR = 1.19 [95% CI = 1.03–1.37], p-value = 0.02; OR = 1.21 [95% CI = 1.05–1.39], p-value = 0.01; respectively) and mortality (OR = 0.78 [95% CI = 0.64–0.96], p-value = 0.02) whereas Tenecteplase 0.40 mg/kg increases the chances of symptomatic intracranial hemorrhage (OR = 2.35 [95% CI = 1.19–4.64], p-value = 0.01).

          Conclusion:

          While not conclusive, our study lends evidence to 0.25 mg/kg Tenecteplase dose for ischemic stroke treatment. Further randomized trials need to be done to validate this finding.

          Registration:

          International prospective register of systematic reviews (PROSPERO) – CRD42022339774 URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=339774

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          Most cited references42

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            How to perform a meta-analysis with R: a practical tutorial

            Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.
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              GRADE guidelines: 4. Rating the quality of evidence--study limitations (risk of bias).

              In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if most of the relevant evidence comes from studies that suffer from a high risk of bias. Well-established limitations of randomized trials include failure to conceal allocation, failure to blind, loss to follow-up, and failure to appropriately consider the intention-to-treat principle. More recently recognized limitations include stopping early for apparent benefit and selective reporting of outcomes according to the results. Key limitations of observational studies include use of inappropriate controls and failure to adequately adjust for prognostic imbalance. Risk of bias may vary across outcomes (e.g., loss to follow-up may be far less for all-cause mortality than for quality of life), a consideration that many systematic reviews ignore. In deciding whether to rate down for risk of bias--whether for randomized trials or observational studies--authors should not take an approach that averages across studies. Rather, for any individual outcome, when there are some studies with a high risk, and some with a low risk of bias, they should consider including only the studies with a lower risk of bias. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                European Stroke Journal
                European Stroke Journal
                SAGE Publications
                2396-9873
                2396-9881
                March 2023
                October 13 2022
                March 2023
                : 8
                : 1
                : 93-105
                Affiliations
                [1 ]University Hospitals of Leicester NHS Trust, Leicester, UK
                [2 ]University of Medicine 1, Yangon, Myanmar
                [3 ]Division of Neurology, Department of Medicine, National University Hospital, Singapore
                Article
                10.1177/23969873221129924
                351b4036-d633-4d14-8e42-f58ddff54433
                © 2023

                https://journals.sagepub.com/page/policies/text-and-data-mining-license

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