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      Safety and efficacy of Cerebrolysin in early post-stroke recovery: a meta-analysis of nine randomized clinical trials

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          Abstract

          This meta-analysis combines the results of nine ischemic stroke trials, assessing efficacy of Cerebrolysin on global neurological improvement during early post-stroke period. Cerebrolysin is a parenterally administered neuropeptide preparation approved for treatment of stroke. All included studies had a prospective, randomized, double-blind, placebo-controlled design. The patients were treated with 30–50 ml Cerebrolysin once daily for 10–21 days, with treatment initiation within 72 h after onset of ischemic stroke. For five studies, original analysis data were available for meta-analysis (individual patient data analysis); for four studies, aggregate data were used. The combination by meta-analytic procedures was pre-planned and the methods of synthesis were pre-defined under blinded conditions. Search deadline for the present meta-analysis was December 31, 2016. The nonparametric Mann-Whitney (MW) effect size for National Institutes of Health Stroke Scale (NIHSS) on day 30 (or 21), combining the results of nine randomized, controlled trials by means of the robust Wei-Lachin pooling procedure (maximin-efficient robust test), indicated superiority of Cerebrolysin as compared with placebo (MW 0.60, P < 0.0001, N = 1879). The combined number needed to treat for clinically relevant changes in early NIHSS was 7.7 (95% CI 5.2 to 15.0). The additional full-scale ordinal analysis of modified Rankin Scale at day 90 in moderate to severe patients resulted in MW 0.61 with statistical significance in favor of Cerebrolysin (95% CI 0.52 to 0.69, P = 0.0118, N = 314). Safety aspects were comparable to placebo. Our meta-analysis confirms previous evidence that Cerebrolysin has a beneficial effect on early global neurological deficits in patients with acute ischemic stroke.

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          The online version of this article (10.1007/s10072-017-3214-0) contains supplementary material, which is available to authorized users.

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          Individual Comparisons by Ranking Methods

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          Article 0 comments Cited 2041 times – based on 0 reviews     Review now
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            Meta-analysis of continuous outcomes combining individual patient data and aggregate data.

            Richard Riley, Paul Lambert, Jan Staessen … (2008)
            Meta-analysis of individual patient data (IPD) is the gold-standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta-analysis of continuous outcomes from randomized controlled trials. The methods take either a one-step or a two-step approach. The latter is simple, with IPD reduced to AD so that standard AD meta-analysis techniques can be employed. The one-step approach is more complex but offers a flexible framework to include both patient-level and trial-level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient-level covariates modify treatment effect, as aggregate-level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within-trial and across-trials treatment-covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between-study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright (c) 2007 John Wiley & Sons, Ltd.
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              Novel end point analytic techniques and interpreting shifts across the entire range of outcome scales in acute stroke trials.

              Jeffrey L Saver (2007)
              Stroke treatments are generally not curative, but rather alter patient outcome over the entire range of functional measures. Dichotomizing outcome scales reduces computational complexity, but discards substantial outcome information, artificially privileges only a single health state transition as clinically meaningful, and often reduces study power. Newer approaches to endpoint analysis have several advantageous properties. Summary of Review- The global statistic assesses treatment effects on multiple outcome measures simultaneously. However, translating the global statistic multidimensional vector effect at the population level into benefit or harm expected in the individual patient is problematic. Responder analysis adjusts outcome thresholds to patient stroke severity at study entry, identifying achievable goals for each patient. However, responder analysis still discards substantial outcome information. Shift analysis gauges change in outcome distributions over the full range of ascertained outcomes, incorporating benefit and harm at all health state transitions valued by patients and clinicians, and often increasing study power. Translation of findings of shift analyses into clinically accessible terms may be accomplished using the recently developed joint outcome table specification technique, which yields the following values for the number needed to treat for 1 patient to improve in a clinically important manner: nimodipine in subarachnoid hemorrhage, 6.8; coiling over clipping, 5.9; intra-arterial pro-urokinase in acute cerebral ischemia, 4.8; intravenous tissue plasminogen activator, 3.3. Dichotomized, global statistic, responder, and shift analyses each offer distinctive benefits and drawbacks. Choice of primary end point analytic technique should be tailored to the study population, expected treatment response, and study purpose. Shift analysis generally provides the most comprehensive index of a treatment's clinical impact.
              Article 0 comments Cited 51 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Dafin Muresanu: dafinm@ssnn.ro
                Journal
                Journal ID (nlm-ta): Neurol Sci
                Journal ID (iso-abbrev): Neurol. Sci
                Title: Neurological Sciences
                Publisher: Springer Milan (Milan )
                ISSN (Print): 1590-1874
                ISSN (Electronic): 1590-3478
                Publication date (Electronic): 16 December 2017
                Publication date PMC-release: 16 December 2017
                Publication date (Print): 2018
                Volume: 39
                Issue: 4
                Pages: 629-640
                Affiliations
                [1 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, Shaare Zedek Medical Center, Jerusalem, and Sackler Faculty of Medicine, , Tel Aviv University, ; Tel Aviv, Israel
                [2 ]ISNI 0000 0000 9559 0613, GRID grid.78028.35, Russian National Research Medical University and Moscow Research and Clinical Center for Neuropsychiatry, ; Ul. Donskaya 43, Moscow, 115419 Russia
                [3 ]Department of Biometry and Clinical Research, IDV Data Analysis and Study Planning, Konrad-Zuse-Bogen 17, 82152 Krailling, Germany
                [4 ]ISNI 0000 0004 4911 0702, GRID grid.418034.a, Max Planck Institute for Metabolism Research, ; Gleueler Street 50, 50931 Cologne, Germany
                [5 ]ISNI 0000 0000 9559 0613, GRID grid.78028.35, Russian National Research Medical University, ; Moscow, Russia
                [6 ]Department of Neurology, SRH Gesundheitszentrum Bad Wimpfen GmbH, Bad Wimpfen, Germany
                [7 ]ISNI 0000 0000 9828 7548, GRID grid.8194.4, Department of Neurology, , “Carol Davila” University of Medicine and Pharmacy, ; Bulevardul Eroii Sanitari 8, 050474 Bucharest, Romania
                [8 ]ISNI 0000 0004 0369 4968, GRID grid.433858.1, Laboratory of Molecular Biology, , “Victor Babes” National Institute of Pathology, ; Bucharest, Romania
                [9 ]ISNI 0000 0004 0571 5814, GRID grid.411040.0, Department of Clinical Neurosciences, , “Iuliu Hatieganu” University of Medicine and Pharmacy, ; Victor Babes Street No. 8, 400012 Cluj-Napoca, Romania
                [10 ]”RoNeuro” Institute for Neurological Research and Diagnostic, 37 Mircea Eliade Street, 400364 Cluj-Napoca, Romania
                Article
                Publisher ID: 3214
                DOI: 10.1007/s10072-017-3214-0
                PMC ID: 5884916
                PubMed ID: 29248999
                SO-VID: f505749c-2ccf-4d35-b4fa-d4c0fed8536b
                Copyright © © The Author(s) 2017
                License:

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Date received : 16 October 2017
                Date accepted : 29 November 2017
                Categories
                Subject: Review Article
                Custom metadata
                issue-copyright-statement © Springer-Verlag Italia S.r.l., part of Springer Nature 2018

                ScienceOpen disciplines: Neurosciences
                Keywords: cerebrolysin,stroke,recovery,early benefit,nihss,meta-analysis
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: cerebrolysin, stroke, recovery, early benefit, nihss, meta-analysis

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