Application of GRADE: Making evidence-based recommendations about diagnostic tests in clinical practice guidelines

BackgroundAccurate diagnosis is a fundamental aspect of appropriate healthcare. However, clinicians need guidance when implementing diagnostic tests given the number of tests available and resource constraints in healthcare. Practitioners of health often feel compelled to implement recommendations in guidelines, including recommendations about the use of diagnostic tests. However, the understanding about diagnostic tests by guideline panels and the methodology for developing recommendations is far from completely explored. Therefore, we evaluated the factors that guideline developers and users need to consider for the development of implementable recommendations about diagnostic tests.MethodsUsing a critical analysis of the process, we present the results of a case study using the Grading of Recommendations Applicability, Development and Evaluation (GRADE) approach to develop a clinical practice guideline for the diagnosis of Cow Milk Allergy with the World Allergy Organization.ResultsTo ensure that guideline panels can develop informed recommendations about diagnostic tests, it appears that more emphasis needs to be placed on group processes, including question formulation, defining patient-important outcomes for diagnostic tests, and summarizing evidence. Explicit consideration of concepts of diagnosis from evidence-based medicine, such as pre-test probability and treatment threshold, is required to facilitate the work of a guideline panel and to formulate implementable recommendations.DiscussionThis case study provides useful guidance for guideline developers and clinicians about what they ought to demand from clinical practice guidelines to facilitate implementation and strengthen confidence in recommendations about diagnostic tests. Applying a structured framework like the GRADE approach with its requirement for transparency in the description of the evidence and factors that influence recommendations facilitates laying out the process and decision factors that are required for the development, interpretation, and implementation of recommendations about diagnostic tests.