The use of high-fidelity manikins for advanced life support training--A systematic review and meta-analysis

NA-AC-High Fidelity Mannequins


Objectives: The objective of   this study was to evaluate the effectiveness of high versus low fidelity   manikins in the context of advanced life support training for improving   knowledge, skill performance at course conclusion, skill performance between   course conclusion and one year, skill performance at one year, skill   performance in actual resuscitations, and patient outcomes. 

Methods: A systematic search of   Pubmed, Embase and Cochrane databases was conducted through January 31, 2014.   We included two-group non-randomized and randomized studies in any language   comparing high versus low fidelity manikins for advanced life support training.   Reviewers worked in duplicate to extract data on learners, study design, and   outcomes. The GRADE (Grades of Recommendation, Assessment, Development and   Evaluation) approach was used to evaluate the overall quality of evidence for   each outcome. 

Results: 3840 papers were identified from the literature search of which 14 were   included (13 randomized controlled trials; 1 non-randomized controlled   trial). Meta-analysis of studies reporting skill performance at course   conclusion demonstrated a moderate benefit for high fidelity manikins when   compared with low fidelity manikins [Standardized Mean Difference 0.59; 95%   CI 0.13-1.05]. Studies measuring skill performance at one year, skill   performance between course conclusion and one year, and knowledge demonstrated   no significant benefit for high fidelity manikins. 

Conclusion: The use of high fidelity manikins for advanced life support   training is associated with moderate benefits for improving skills   performance at course conclusion. Future research should define the optimal   means of tailoring fidelity to enhance short and long term educational goals   and clinical outcomes.

Authors: Adam Cheng, Andrew Lockey, Farhan Bhanji, Yiquin Lin, Elizabeth A. Hunt, Eddy Lang

Adam Cheng -

Preliminary data gathering / Baseline