Deconstructing resuscitation training for healthcare providers: a protocol for a component network meta-analysis

Area of Science:

• Medical Education
• Cardiopulmonary Resuscitation
• Evidence-Based Practice

Background:

• International guidelines emphasize improving resuscitation training to decrease mortality, disability, and costs.
• Resuscitation training involves complex, multifaceted approaches requiring identification of optimal components and participant groups.
• Understanding the effectiveness of different training strategies is crucial for healthcare professionals.

Purpose of the Study:

• To systematically identify and evaluate the effectiveness of various resuscitation training methods and their components.
• To determine the effect sizes of different training interventions and combinations.
• To ascertain which participant groups benefit most from specific resuscitation training approaches.

Main Methods:

• Systematic search of 12 databases and 2 clinical trial registries for randomized controlled trials (RCTs) up to April 2025.
• Application of standard and component network meta-analysis models using frequentist and Bayesian approaches in R.
• Decomposition of interventions into components (e.g., delivery method, interactivity, teamwork, digitalization, simulator type) for detailed analysis.

Main Results:

• Primary outcome: improvement in resuscitation skills of healthcare staff.
• Risk of bias assessed using RoB V.2.0.
• Certainty of evidence evaluated with Confidence in Network Meta-Analysis.

Conclusions:

• This comprehensive network meta-analysis will provide evidence-based recommendations for optimizing resuscitation training programs.
• Findings will inform the development of more effective and cost-efficient resuscitation education strategies.
• Dissemination through peer-reviewed publications and conference presentations is planned.