GRADE guidance 39: using GRADE-ADOLOPMENT to adopt, adapt or create contextualized recommendations from source guidelines and evidence syntheses

Abstract

Background and Objective: The Grading of Recommendations, Assessment, Development and Evaluations (GRADE)-ADOLOPMENT methodology has been widely used to adopt, adapt, or de novo develop recommendations from existing or new guideline and evidence synthesis efforts. The objective of this guidance is to refine the operationalization for applying GRADE-ADOLOPMENT. Methods: Through iterative discussions, online meetings, and email communications, the GRADE-ADOLOPMENT project group drafted the updated guidance. We then conducted a review of handbooks of guideline-producing organizations, and a scoping review of published and planned adolopment guideline projects. The lead authors refined the existing approach based on the scoping review findings and feedback from members of the GRADE working group. We presented the revised approach to the group in November 2022 (approximately 115 people), in May 2023 (approximately 100 people), and twice in September 2023 (approximately 60 and 90 people) for approval. Results: This GRADE guidance shows how to effectively and efficiently contextualize recommendations using the GRADE-ADOLOPMENT approach by doing the following: (1) showcasing alternative pathways for starting an adolopment effort; (2) elaborating on the different essential steps of this approach, such as building on existing evidence-to-decision (EtDs), when available or developing new EtDs, if necessary; and (3) providing examples from adolopment case studies to facilitate the application of the approach. We demonstrate how to use contextual evidence to make judgments about EtD criteria, and highlight the importance of making the resulting EtDs available to facilitate adolopment efforts by others. Conclusion: This updated GRADE guidance further operationalizes the application of GRADE-ADOLOPMENT based on over 6 years of experience. It serves to support uptake and application by end users interested in contextualizing recommendations to a local setting or specific reality in a short period of time or with limited resources.