Deconstructing resuscitation training for healthcare providers: a protocol for a component network meta-analysis.
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BORIS DOI
Date of Publication
July 25, 2025
Publication Type
Article
Division/Institute
Author
Efendi, Defi | |
Cheng, Adam | |
Wanda, Dessie | |
Furukawa, Toshi A | |
Petropoulou, Maria | |
Chen, Kee-Hsin |
Subject(s)
Series
BMJ Open
ISSN or ISBN (if monograph)
2044-6055
Publisher
BMJ Publishing Group
Language
English
Publisher DOI
PubMed ID
40713054
Description
Introduction
The necessity of enhancing resuscitation training has been encouraged by The International Liaison Committee on Resuscitation and the American Heart Association to reduce mortality, disability and healthcare costs. Resuscitation training is a complicated approach that encompasses various components and their mixture. It is essential to identify the most effective of these components and their combinations, to measure the corresponding effect size and to understand which participant groups may enjoy the greatest advantage.Methods And Analysis
We will systematically search 12 databases and two clinical trial registries for randomised controlled trials (RCTs) that examine different resuscitation training methods from inception to April 2025. The analysis will be carried out using the standard network meta-analysis and component network meta-analysis models. Resuscitation skills of staff will be the primary outcome of this analysis. Paired reviewers will independently screen and extract data. A consensus will be sought with the principal investigators to resolve any disagreements that cannot be achieved through regular meetings. Each intervention in each RCT will be decomposed according to its constituent components, such as delivery method, interactivity, teamwork, digitalisation and type of simulator. The analysis will be conducted using the frequentist and bayesian approach in the R environment. RoB V.2.0 and Confidence in Network Meta-Analysis will, respectively, be used to assess the risk of bias and the certainty of the evidence.Ethics And Dissemination
As we will use only aggregated secondary data without individual identities, ethical approval is not required. Results of this review will be shared through a peer-reviewed publication and presentation of papers at any relevant conferences.Prospero Registration Number
CRD42024532878.
The necessity of enhancing resuscitation training has been encouraged by The International Liaison Committee on Resuscitation and the American Heart Association to reduce mortality, disability and healthcare costs. Resuscitation training is a complicated approach that encompasses various components and their mixture. It is essential to identify the most effective of these components and their combinations, to measure the corresponding effect size and to understand which participant groups may enjoy the greatest advantage.Methods And Analysis
We will systematically search 12 databases and two clinical trial registries for randomised controlled trials (RCTs) that examine different resuscitation training methods from inception to April 2025. The analysis will be carried out using the standard network meta-analysis and component network meta-analysis models. Resuscitation skills of staff will be the primary outcome of this analysis. Paired reviewers will independently screen and extract data. A consensus will be sought with the principal investigators to resolve any disagreements that cannot be achieved through regular meetings. Each intervention in each RCT will be decomposed according to its constituent components, such as delivery method, interactivity, teamwork, digitalisation and type of simulator. The analysis will be conducted using the frequentist and bayesian approach in the R environment. RoB V.2.0 and Confidence in Network Meta-Analysis will, respectively, be used to assess the risk of bias and the certainty of the evidence.Ethics And Dissemination
As we will use only aggregated secondary data without individual identities, ethical approval is not required. Results of this review will be shared through a peer-reviewed publication and presentation of papers at any relevant conferences.Prospero Registration Number
CRD42024532878.
File(s)
File | File Type | Format | Size | License | Publisher/Copright statement | Content | |
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e094869.full.pdf | text | Adobe PDF | 478.56 KB | published |