GRADE Notes 4: How to use GRADE when there is “no” evidence? A case study of using unpublished registry data

• Published in: Journal of clinical epidemiology p. 111578
• Main Authors: El Mikati, Ibrahim K., Begin, Brandy, Borzych-Duzalka, Dagmara, Neu, Alicia M., Richardson, Troy, Same, Rebecca G., Schaefer, Franz, Warady, Bradley A., Mustafa, Reem A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 21.10.2024
• Subjects: data; evidence; GRADE; guideline; registry; systematic review; registry; GRADE; data; evidence; systematic review; guideline
• ISSN: 0895-4356; 1878-5921; 1878-5921
• DOI: 10.1016/j.jclinepi.2024.111578

Trustworthy guidelines rely on systematic reviews of the best available published evidence. The GRADE Working Group has provided guidance about developing evidence-based recommendations when published direct evidence is lacking. In this paper, we provide a case example as an alternate solution to generate primary data using registries prior to collecting expert evidence. When direct published literature was absent, a team of clinical and statistical expertise can utilize registries, when available, for primary data generation in a way that allows for answering clinically important questions. Out of 54 questions prioritized by a guideline development for the prevention and management of peritoneal dialysis associated infections in children, 25 questions had no evidence to inform them. The use of unpublished registry data served as a primary source of information to answer 12 of the 25 questions and provided additional information for 9 questions for which at least one published study was available. This paper extends our previous GRADE note for scenarios of “no” evidence, highlighting the value of generating primary evidence using unpublished registry data when relevant registries and resources allow. This approach can of be of particular value when addressing conditions that are rare or from populations that are considered vulnerable, while emphasizing the importance of being transparent regarding the reporting of raw data and the analysis plan in the event of reporting unpublished work.