Login / Signup

Review article: Developing the Australian and New Zealand Guideline for Mild to Moderate Head Injuries in Children: An adoption/adaption approach.

Emma J TavenderDustin W BallardAgnes WilsonMeredith L BorlandEd OakleyElizabeth CotterellCatherine L WilsonJenny RingStuart R DalzielFranz E Bablnull null
Published in: Emergency medicine Australasia : EMA (2021)
The Paediatric Research in Emergency Departments International Collaborative (PREDICT) released the Australian and New Zealand Guideline for Mild to Moderate Head Injuries in Children in 2021. We describe innovative and practical methods used to develop this guideline. Informed by GRADE-ADOLOPMENT and ADAPTE frameworks, we adopted or adapted recommendations from multiple high-quality guidelines or developed de novo recommendations. A Guideline Steering Committee and a multidisciplinary Guideline Working Group of 25 key stakeholder representatives formulated the guideline scope and developed 33 clinical questions. We identified four relevant high-quality source guidelines; their recommendations were mapped to clinical questions. The choice of guideline recommendation, if more than one guideline addressed a question, was based on its appropriateness, currency of the literature, access to evidence, and relevance. Updated literature searches identified 440 new studies and key new evidence identified. The decision to develop adopted, adapted or de novo recommendations was based on the supporting evidence-base and its transferability to the local setting. The guideline underwent a 12-week consultation period. The final guideline consisted of 35 evidence-informed and 17 consensus-based recommendations and 19 practice points. An algorithm to inform imaging and observation decision-making was also developed. The resulting process was an efficient and rigorous way to develop a guideline based on existing high-quality guidelines from different settings.
Keyphrases
  • clinical practice
  • decision making
  • systematic review
  • emergency department
  • machine learning
  • randomized controlled trial
  • healthcare
  • primary care
  • mass spectrometry
  • study protocol
  • neural network