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      Effects of cardiopulmonary resuscitation training on mortality rates after out-of-hospital cardiac arrest: protocol for a systematic review and meta-analysis

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          Abstract

          Introduction

          An out-of-hospital cardiac arrest occurs at a rate of 67–170 cases per 100 000 inhabitants per year in Europe. The early recognition of the occurrence of a cardiac arrest, placing an emergency call, performing cardiopulmonary resuscitation (CPR) and performing defibrillation are the most important response measures. The objective of this systematic review and meta-analysis is to assess the effects of laypersons’ CPR training with respect to CPR initiation rates, cardiovascular mortality rates, survival rate and the use of an automated external defibrillator.

          Methods and analysis

          The literature search will be performed in the following databases: MEDLINE, Web of Science, the Cochrane Central Register of Controlled Studies, CINAHL, HBI, TESEO and NTX. Intervention studies and quasi-experimental studies in which CPR training interventions were performed will be included. We will exclude studies in which the participants do not meet the inclusion criteria, without a control group and in which the methodology of the intervention applied is unclear. There will be no restrictions on publication date or language of publication. The risk of bias will be assessed using the Risk of Bias in Non-randomized Studies of Interventions tool for randomised controlled trials (RCT), non-RCT and quasi-experimental trials. Data analysis and synthesis will be performed using RevMan V.5.4.1 software. The findings will be reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance.

          Ethics and dissemination

          Ethical approval is not required, as only secondary data will be used. The findings will be published in a journal and presented at conferences.

          PROSPERO registration number

          CRD42022365288.

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          Most cited references13

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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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            Rayyan—a web and mobile app for systematic reviews

            Background Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Results Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
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              ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

              Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2024
                29 February 2024
                : 14
                : 2
                : e081525
                Affiliations
                [1 ] departmentHealth Sciences , UCAM Universidad Católica de Murcia , Murcia, Spain
                [2 ] departmentPostgraduate in Public Health , Ringgold_28123UFRN , Natal, Brazil
                [3 ] departmentDepartament of Public Health , Federal University of Rio Grande do Norte , Natal, Brazil
                Author notes
                [Correspondence to ] Dr Manuel Pardo Ríos; mpardo@ 123456ucam.edu
                Author information
                http://orcid.org/0000-0002-2568-2007
                http://orcid.org/0000-0003-4968-1364
                http://orcid.org/0000-0002-2343-7251
                Article
                bmjopen-2023-081525
                10.1136/bmjopen-2023-081525
                10910507
                38423775
                a57c456e-68a4-4a8b-b7e4-0a2bbfe06216
                © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 30 October 2023
                : 16 February 2024
                Funding
                Funded by: Design of a Metaverse for Higher Education in Health Sciences;
                Award ID: PID2022-138884NA-I00
                Funded by: Ignacio H. Larramendi Research Grant by the MAPFRE Foundation;
                Award ID: Finance Code: 2022
                Categories
                Emergency Medicine
                1506
                1691
                Protocol
                Custom metadata
                unlocked

                Medicine
                accident & emergency medicine,cardiopulmonary resuscitation,mortality
                Medicine
                accident & emergency medicine, cardiopulmonary resuscitation, mortality

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