Development of Preliminary Remission Criteria for Gout Using Delphi and 1000Minds Consensus Exercises. Issue 5 (May 2016) - de Lautour, Hugh; Taylor, William J.; Adebajo, Ade; Alten, Rieke; Burgos‐Vargas, Ruben; Chapman, Peter; Cimmino, Marco A.; da Rocha Castelar Pinheiro, Geraldo; Day, Ric; Harrold, Leslie R.; Helliwell, Philip; Janssen, Matthijs; et al.
Abstract : Objective: To establish consensus for potential remission criteria to use in clinical trials of gout. Methods: Experts (n = 88) in gout from multiple countries were invited to participate in a web‐based questionnaire study. Three rounds of Delphi consensus exercises were conducted using SurveyMonkey, followed by a discrete‐choice experiment using 1000Minds software. The exercises focused on identifying domains, definitions for each domain, and the timeframe over which remission should be defined. Results: There were 49 respondents (56% response) to the initial survey, with subsequent response rates ranging from 57% to 90%. Consensus was reached for the inclusion of serum urate (98% agreement), flares (96%), tophi (92%), pain (83%), and patient global assessment of disease activity (93%) as measurement domains in remission criteria. Consensus was also reached for domain definitions, including serum urate (<0.36 mm), pain (<2 on a 10‐point scale), and patient global assessment (<2 on a 10‐point scale), all of which should be measured at least twice over a set time interval. Consensus was not achieved in the Delphi exercise for the timeframe for remission, with equal responses for 6 months (51%) and 1 year (49%). In the discrete‐choice experiment, there was a preference towards 12 months as a timeframe for remission. Conclusion: These consensus exercises have identified domains and provisional definitions for gout remission criteria. Based on the results of these exercises, preliminary remission criteria are proposed with domains of serum urate, acute flares, tophus, pain, and patient global assessment. These preliminary criteria now require testing in clinical data sets.