The timing of outcome measures indicates the approximate target for the review. It is based on the conventional timing used in the assessment of these outcomes.
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Neurological function assessed with the National Institutes of Health Stroke Scale (NIHSS) ( Harrison 2013 ). We assessed this at the first 24 hours (acute phase), at 72 hours, and at discharge. See Appendix 3 for details.
Degree of disability or dependence in daily activities according to the modified Rankin scale (at 90 days) ( Harrison 2013 van Swieten 1988 ). See Appendix 1
Citicoline administered at any dose, by any route, and for any duration of treatment, versus no intervention, placebo, or other interventions. Since acute ischemic stroke requires a variety of medical treatments (that is, primary interventions), we considered citicoline as a supplementary intervention. Thus, for the purpose of this review, eligible RCTs were those that compared the same primary interventions with and without citicoline supplementation.
People (children or adults) with acute ischemic stroke, irrespective of etiology. We used clinical diagnosis with imaging as an eligibility criterion.
We included randomized controlled trials (RCTs) irrespective of publication status. We did not apply any limitation by language, country, or duration of follow‐up. We only included parallel‐design trials.
We screened the reference lists of relevant studies and use Cited Reference Search within Web of Science to identify further studies for potential inclusion in the review, and we contacted trialists and companies for further information.
In order to identify unpublished information submitted for the marketing approval of citicoline, we also searched the following sites.
With the assistance of the Cochrane Stroke Group's Information Specialist, we designed the search strategy in MEDLINE and adapted it to all other databases ( Appendix 5 ). We combined all search strategies deployed with subject strategy adaptations of the Highly Sensitive Search Strategy designed by Cochrane for identifying randomized controlled trials and controlled clinical trials (as described in the Cochrane Handbook for Systematic Reviews of Interventions Chapter 6: Lefebvre 2011 ).
See the methods for the Cochrane Stroke Group Specialised register. We searched for trials in all languages and arranged for translation of relevant articles where necessary.
We conducted data collection and analysis of data according to the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a; Higgins 2011b).
Two review authors (AMC, CV) independently screened titles and abstracts of the references obtained as a result of our searching activities, and excluded obviously irrelevant reports. We retrieved the full‐text articles for the remaining references and, independently, two or more review authors (AMC, CV, IS) screened the full‐text articles and identified studies for inclusion. They also identified and recorded reasons for exclusion of the ineligible studies. We resolved any disagreements through discussion or, if required, we consulted a third review author (JMF). We collated multiple reports of the same study so that each study, not each reference, was the unit of interest in the review. We recorded the selection process and completed a PRISMA flow diagram.
Two review authors (AMC, CV) independently extracted data from included studies. We developed an Excel spreadsheet based on the 'Data extraction template for included studies' from the Consumers and Communication Group resources for authors. We planned to describe the details of the intervention following recommendations from Hoffmann 2014 and Hoffmann 2017.
Two review authors (AMC, CV) independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011c). We resolved any disagreements by discussion or by involving another review author (JMF, XB). We assessed the risk of bias according to the following domains.
Random sequence generation
Allocation concealment
Blinding of participants and personnel
Blinding of outcome assessment
Incomplete outcome data
Selective outcome reporting
Other bias
We graded the risk of bias for each domain as high, low, or unclear and provide information from the study report together with a justification of our judgment in the 'Risk of bias' tables.
We included company funding, bias in the presentation of the data, design bias, measurements and confounding biases under 'Other bias'. See Porta 2014 for definitions of the examples of biases.
See Appendix 12 for details of domains.
For binary outcomes in this review, such as all‐cause mortality and adverse events, we calculated the risk ratio (RR) with 95% confidence intervals (CIs).
For future updates we will follow this approach: for continuous outcomes, such as functional outcome, degree of disability or dependence in daily activities, and neurological, behavioral and cognitive function, we plan to calculate the mean difference (MD) with 95% CI. If ordinal data are reported, we will use a proportional odds model as a measure of treatment effect with Stata statistical software (STATA) (Bath 2012: Deeks 2017; Scott 1997). If different scales are used for measuring the same outcome, for example quality of life, we plan to use the standardized mean difference (SMD) with 95% CI. We will also estimate ratio of means (RoM) with 95% CI from mean difference (Friedrich 2011). Due to practitioners' understanding and preferring dichotomous presentations of continuous outcomes, which they perceive to be the most useful (Johnston 2016), we will estimate odds ratios (OR) with 95% CI and the number needed to treat for an additional beneficial outcome (NNTB) from SMD with Furukawa's method (Furukawa 1999; Furukawa 2011).
As recommended in section 9.2.3.2 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a), if necessary we will multiply the mean values from one set of studies by −1 to ensure that all the scales point in the same direction (Deeks 2017).
If statistical information is missing (such as standard deviations), we will try to extract them from other relevant information in the paper, such as P values and CIs.
We will calculate the NNTB if the RR was significant (P value < 0.05). NNTB is a measure of assessment of clinical useful of the consequences of treatment (Laupacis 1988). We will estimate NNTB with GraphPad software and with the Cochrane Stroke Group NNT calculator. If ordinal data are reported, we will estimate NNTB according to Bath 2011.
The unit of analysis was participants. We excluded cluster RCTs.
As recommended in the Cochrane Handbook for Systematic Reviews of Interventions, we conducted the following plan to assess the outcomes with multiple observations.
For primary outcomes (all‐cause mortality and degree of disability or dependence in daily activities according to the modified Rankin scale) and secondary outcomes (adverse events, functional recovery and quality of life), we selected a single time point and analyzed only data at this time for trials in which it will be presented.
Neurological function assessed with the National Institutes of Health Stroke Scale (secondary outcome): we planned to define different periods of follow‐up (short‐term, medium‐term, and long‐term follow‐up) ( Deeks 2017 ), and perform separate analyses.
We assessed the percentage of dropouts for each intervention group for each included trial, and evaluated whether an intention‐to‐treat (ITT) analysis was performed or could have been performed from the available published information. We contacted study authors to resolve any questions arising from this issue.
In order to undertake an ITT analysis, we sought data from the trial authors about the number of participants in treatment groups, irrespective of their compliance and whether or not they were later thought to be ineligible, otherwise excluded from treatment, or lost to follow‐up. If this information was not forthcoming, we performed a 'per protocol' analysis of those who completed the study, being aware that it may be biased.
We included participants with incomplete or missing data in sensitivity analyses by imputing them according to the following scenarios (Hollis 1999).
Extreme case analysis favoring the experimental intervention ('best‐worse' case scenario): none of the drop‐outs/participants lost from the experimental arm, but all of the drop‐outs/participants lost from the control arm experienced the outcome, including all randomized participants in the denominator.
Extreme case analysis favoring the control ('worst‐best' case scenario): all drop‐outs/participants lost from the experimental arm, but none from the control arm experienced the outcome, including all randomized participants in the denominator.
Gamble‐Hollis analysis, which takes account of the uncertainty and generates uncertainty intervals for a trial incorporating both sampling error and the potential impact of missing data ( Gamble 2005 ). This method increases the uncertainty of the trials using the results from the best‐case and worst‐case analyses ( Chaimani 2014 ).
We quantified statistical heterogeneity using the I² statistic, which describes the percentage of total variation across trials that is due to heterogeneity rather than sampling error (Higgins 2003). We had set an I² threshold greater than 60% to consider the presence of statistical heterogeneity (Deeks 2017).
We did not identify 10 or more RCTs to conduct the assessment of reporting biases for any outcome. For the future update, therefore, we will use the contour‐enhanced funnel plot to differentiate asymmetry that is due to publication bias from that due to other factors (Peters 2008). We will assess likelihood of publication bias with Harbord's and Peters' tests (Sterne 2011). We will use Stata statistical software to produce conventional and contour funnel plots (STATA).
We developed a 'Summary of findings' table with the following outcomes: all‐cause mortality; degree of disability or dependence in daily activities on the modified Rankin scale; adverse events; functional recovery (Barthel Index); neurological function (National Institutes of Health Stroke Scale: NIHSS); and quality of life ( ). We assessed the quality of the evidence for each outcome according to GRADE domains (study limitations, consistency of effect, imprecision, indirectness and publication bias) (Atkins 2004). We used methods and recommendations described in Section 8.5 and Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2011a), and GRADEpro GDT software (GRADEpro GDT 2015). We justified all decisions to downgrade the quality of the evidence using footnotes, and we made comments to aid the reader's understanding of the review where necessary. We calculated the assumed control group risks using the median control group risk (Schünemann 2011b).
We conducted the following subgroup analysis for primary outcomes if more than five trials were included.
Trials supported by pharmaceutical companies versus trials without support by pharmaceutical companies.
Trials with low risk of bias versus trials with high risk of bias.
Trials with small sample size (≤ 200 participants) versus trials with large sample size (> 200 participants).
Due to lack of data, we were not able to conduct subgroup analysis with:
participants with diabetes mellitus versus participants without diabetes mellitus;
participants with high blood pressure versus participants without high blood pressure.
We performed sensitivity analysis for primary outcomes with Stata statistical software (STATA), in order to explore the influence of particular factors on the intervention effect size: 'best‐worst case' scenario versus 'worst‐best case' scenario and Gamble‐Hollis analysis (Gamble 2005).
We estimated the threshold for clinical relevance for primary outcomes through use of Bayes factors (Jakobsen 2014). The Bayes factor is a likelihood ratio that indicates the relative strength of evidence for two theories (Dienes 2014; Dienes 2018Goodman 1999; Goodman 2005). The Bayes factor is a comparison of how well two hypotheses (the null hypothesis ‒ H0; and the alternative hypothesis ‐ H1) predict the data (Goodman 1999). The Bayes factor provides a continuous measure of evidence for H1 over H0. When the Bayes factor is 1, evidence is insensitive, and this means that the data are equally well predicted by both models and the evidence does not favor either model over the other (1 means the data are as well predicted by H1 as H0, so it should not be interpreted as favoring H0; rather the evidence does not point either way). As the Bayes factor increases above 1 (towards infinity) the evidence favors H1 over H0. As the Bayes factor decreases below 1 (towards 0) the evidence favors H0 over H1 (Dienes 2008; Dienes 2014; Dienes 2018).
Despite the use of Bayes factors, we based the conclusions of this Cochrane Review on the Review Manager 5 analysis (Review Manager 2014).
Our in-depth Report [119 Pages] on the Citicoline Market Provides a Comprehensive and in-depth Analysis Based on Regions, Applications (Injection , Tablets , Capsule ), and Types (Pharma , Non-pharma ). The report presents the research and analysis provided within the Citicoline Market Research is meant to benefit stakeholders, vendors, and other participants in the industry. The Citicoline market is expected to grow annually by magnificent (CAGR 2023-2030).
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Brief Description of Citicoline Market:
The Citicoline Market is Forecasted to Reach a Multimillion-Dollar Valuation by 2030, Exhibiting an Unexpected CAGR During the Forecast Period of 2023-2030, as Compared to Data from 2016 to 2022.
Citicoline Market in order for companies to make suggestions that will support their development and success, reports on market research include the most recent market developments, potential areas for future growth, and information about the competitive context. Over the past few years, the Citicoline market has witnessed remarkable growth, driven by the increasing consumer admiration for its visually pleasing design, long-lasting durability, and environmentally conscious attributes. Welcome to our captivating article where we embark on a journey through the enchanting realm of Citicoline. Join us as we uncover the compelling reasons behind its soaring popularity.
TOP Manufacturers Listed in Citicoline Market Report are: -
The research study includes the current trends that are being adopted by significant market players in the Citicoline markets, including the use of cutting-edge technology, government funding of R&D, and an increasing focus on sustainability. In addition, our research team has provided crucial information to shed light on the manufacturer's position in the national and international marketplaces.
Our report on the global Citicoline market provides deep insights into the market dynamics and trends. This in-depth analysis is essential for decision makers looking to capitalize on the growth opportunities in this market. It provides an overview of the market size, growth opportunities, drivers, and restraints, as well as the competitive landscape. Ask for a Sample Report
The report delves into the geographical distribution of the Citicoline market and identifies key countries that are generating the highest revenues. It also uncovers the latest market trends and forecasting data to provide you with an informed understanding of the global Citicoline market and the future it holds. The report provides an in-depth study of the major players in the market, their competitive landscape, product portfolios, strategies, and market shares. Moreover, the report provides detailed information on the regional segments, applications, and end-user industries. This comprehensive research helps stakeholders to make informed investment decisions and gain maximum returns from their investments.
Based on TYPE, the Citicoline market from 2023 to 2030 is primarily split into:
Based on applications, the Citicoline market from 2023 to 2030 covers:
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Regions are covered:
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The Citicoline Market Research Report provides a detailed analysis of regional markets, covering the period of 2023-2030. It furnishes comprehensive information on the growth, sales, and revenue patterns observed in each region, facilitating a better understanding of the industry's key challenges. Furthermore, the report assesses the market competition among major regions, such as North America, Europe, Asia, the Middle East, Africa, Latin America and the Caribbean, and Oceania.
The COVID 19 pandemic had a significant effect on the industry globally and altered the market environment. The post-and pre-COVID market conditions, the influence on the market, and significant changes in industry as a result of the pandemic are all covered in the report. It also explains why the market was able to bounce back successfully after the COVID 19 hit.
This Citicoline Market Research/Analysis Report Contains Answers to your following Questions
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Detailed TOC of Global Citicoline Market Research Report 2023
1 Citicoline Market Overview
1.1 Product Overview and Scope of Citicoline Market
1.2 Citicoline Market Segment by Type
1.3 Global Citicoline Market Segment by Application
1.4 Global Citicoline Market, Region Wise (2017-2030)
1.5 Global Market Size (Revenue) of Citicoline (2017-2030)
1.6 Influence of Regional Conflicts on the Citicoline Industry
1.7 Impact of Carbon Neutrality on the Citicoline Industry
2 Citicoline Market Upstream and Downstream Analysis
2.1 Citicoline Industrial Chain Analysis
2.2 Key Raw Materials Suppliers and Price Analysis
2.3 Key Raw Materials Supply and Demand Analysis
2.4 Market Concentration Rate of Raw Materials
2.5 Manufacturing Process Analysis
2.6 Manufacturing Cost Structure Analysis
2.7 Major Downstream Buyers of Citicoline Analysis
2.8 Impact of COVID-19 on the Industry Upstream and Downstream
3 Players Profiles
4 Global Citicoline Market Landscape by Player
4.1 Global Citicoline Sales and Share by Player (2017-2022)
4.2 Global Citicoline Revenue and Market Share by Player (2017-2022)
4.3 Global Citicoline Average Price by Player (2017-2022)
4.4 Global Citicoline Gross Margin by Player (2017-2022)
4.5 Citicoline Market Competitive Situation and Trends
4.5.1 Citicoline Market Concentration Rate
4.5.2 Citicoline Market Share of Top 3 and Top 6 Players
4.5.3 Mergers & Acquisitions, Expansion
5 Global Citicoline Sales, Revenue, Price Trend by Type
5.1 Global Citicoline Sales and Market Share by Type (2017-2022)
5.2 Global Citicoline Revenue and Market Share by Type (2017-2022)
5.3 Global Citicoline Price by Type (2017-2022)
5.4 Global Citicoline Sales, Revenue and Growth Rate by Type (2017-2022)
6 Global Citicoline Market Analysis by Application
6.1 Global Citicoline Consumption and Market Share by Application (2017-2022)
6.2 Global Citicoline Consumption Revenue and Market Share by Application (2017-2022)
6.3 Global Citicoline Consumption and Growth Rate by Application (2017-2022)
6.3.1 Global Citicoline Consumption and Growth Rate of Transportation (2017-2022)
6.3.2 Global Citicoline Consumption and Growth Rate of Retailing (2017-2022)
7 Global Citicoline Sales and Revenue Region Wise (2017-2022)
7.1 Global Citicoline Sales and Market Share, Region Wise (2017-2022)
7.2 Global Citicoline Revenue and Market Share, Region Wise (2017-2022)
7.3 Global Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.4 United States Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.5 Europe Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.6 China Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.7 Japan Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.8 India Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.9 Southeast Asia Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.10 Latin America Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
7.11 Middle East and Africa Citicoline Sales, Revenue, Price and Gross Margin (2017-2022)
8 Global Citicoline Market Forecast (2022-2030)
8.2 Global Citicoline Sales and Revenue Forecast, Region Wise (2022-2030)
8.3 Global Citicoline Sales, Revenue and Price Forecast by Type (2022-2030)
8.4 Global Citicoline Consumption Forecast by Application (2022-2030)
8.5 Citicoline Market Forecast Under COVID-19
9 Industry Outlook
9.1 Citicoline Market Drivers Analysis
9.2 Citicoline Market Restraints and Challenges
9.3 Citicoline Market Opportunities Analysis
9.4 Emerging Market Trends
9.5 Citicoline Industry Technology Status and Trends
9.6 News of Product Release
9.7 Consumer Preference Analysis
9.8 Citicoline Industry Development Trends under COVID-19 Outbreak
10 Research Findings and Conclusion
11 Appendix
11.1 Methodology
11.2 Research Data Source
Continued...
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