AB0893 WHAT IS THE CHANCE IN AN INDIVIDUAL GOUTY PATIENTTHAT DISEASE WILL BE STILL ACTIVE IN A YEAR? PREDICTIVE MODELLING MIGHT COME TO AID. (June 2019)

• Record Type: Journal Article
• Authors: Ughi, Nicola; Carrara, Greta; Zanetti, Anna; Cimmino, Marco Amedeo; Manara, Maria; Govoni, Marcello; Salaffi, Fausto; Punzi, Leonardo; Montecucco, Carlomaurizio; Matucci-Cerinic, Marco; Minisola, Giovanni; Zambon, Antonella; Scirè, Carlo Alberto
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 1913
• Page End: 1914
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.4911
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: The treat-to-target concept is currently applied to gout [1] and a composite outcome to measure disease activity may serve as a target in clinical practice. Strategies for achieving the target are still not individualized and the ability to adjust the treatment on an individual basis using prognostic information from predictive models might help practitioners to identify patients with a high or low probability of remission or disease inactivity. Objectives: To develop a prediction model for disease inactivity in gouty patients. Methods: Data were retrieved from the Kick-off of the Italian Network for Gout (KING) observational cohort multicentre study on behalf of the Italian Society for Rheumatology. The gout activity score (GAS) [2, 3] was used as validated outcome to define inactive disease (cut off <2.5) at 12 months in gouty patients with active disease at baseline and receiving the standard of care in two visits 6 months apart. Logistic regression analyses and backwards elimination of baseline predictors with bootstrap resampling were used to develop parsimonious models. Performance and correction for optimism were assessed. Missing data were handled through multiple imputation by chained equations (50 completed datasets). Results: Out of 446 patients enrolled from 30 rheumatology centres, 265 (59%) had active disease at baseline (mean age 63±11 years, male 91%, median disease duration 7 years, tophi 23%, urate lowering therapy 82%, mean serum urate [sUA] levels 6.7±1.6 mg/dl) and a 12-month visit. In 34% (65/206) gout was inactive at 12 months. From 20 candidate predictors (complete cases n=150) disease duration, use of nonsteroidal anti-inflammatory drugs (NSAIDs), number of flares in the previous 12 months, presence of tophi, sUA <6 mg/dl, visual analogue scale (VAS) global patient, and baseline GAS were selected. The performance of the four developed models is shown in table. In the completed case analysis (n = 258) the c-statistics (95% confidence interval [CI]) of models 1 to 4 were 0.79(0.73-0.85), 0.78(0.72-0.84), 0.76(0.70-0.82), and 0.74(0.68-0.81), respectively. Conclusion: These preliminary prediction models for inactive disease showed good performance in gouty patients from a real-life secondary setting. External validation in ad-hoc studies is required to identify the best model and to search for additional predictors and increase its predictive performance. References: [1] Kiltz U, et al. Ann Rheum Dis2017 [2] Sciré CA, et al. Arthritis Care Res (Hoboken)2016 [3] La-Crette J, et al. Joint Bone Spine2018 Disclosure of Interests: Nicola Ughi Speakers bureau: Pfizer, Grunenthal, Greta Carrara: None declared, Anna Zanetti: None declared, Marco Amedeo Cimmino Grant/research support from: Menarini, Consultant for: Menarini International, Maria Manara: None declared, Marcello Govoni Paid instructor for: Pfizer, Roche, Speakers bureau: Pfizer, Abbvie, MSD, Roche, Eli-Lilly, Celgene, Sanofi, Janssen, Fausto Salaffi Grant/research support from: Abbvie, Roche, Novartis, BMS, Pfizer, Sanofi, Speakers bureau: Abbvie, Roche, Novartis, Pfizer, Sanofi, BMS, Leonardo Punzi Consultant for: BMS, Fidia, Grunenthal, Menarini, Speakers bureau: BMS, Fidia, Grunenthal, Menarini, Carlomaurizio Montecucco Speakers bureau: AbbVie, Bristol-Myers Squibb, Celgene, Sanofi, Genzyme, Lilly, MSD, Pfizer, UCB, Marco Matucci-Cerinic Grant/research support from: Actelion, MSD, Pfizer, BMS, Chemomab, Sanipedia, Speakers bureau: Actelion, BMS; MSD, Janssen, giovanni minisola: None declared, Antonella Zambon: None declared, Carlo Alberto Scirè: None declared