THU0081 Towards Individualized Risk Determination in RA: A Prediction Model for TNFI Discontinuation within the First Year After Start. (9th June 2015)

• Record Type: Journal Article
• Authors: Cuppen, B.V.; Jacobs, J.W.; Marijnissen, A.C.; van Laar, J.M.; Lafeber, F.P.
• Is Part Of: Annals of the rheumatic diseases. Volume 74(2015)Supplement 2
• Page Start: 221
• Page End: 221
• Publication Date: 2015-06-09
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2015-eular.3839
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: TNF-alpha inhibitor (TNFi) treatment has dramatically improved the outcome of RA patients. A substantial number of patients, however, fails to clinically respond to this therapy or experiences adverse-effects that necessitate discontinuation of therapy. In that sense, treatment success could be envisioned as a matter of balance between drug efficacy and tolerability. Prediction of discontinuation gives insight in patients at risk for suboptimal treatment success, and possibly factors that interacts the decision making of physician and patient. Objectives: To predict TNFi discontinuation within the first year of use in an observational cohort and gain more insight in parameters predictive of treatment success. Methods: Data was used from the Biologicals and Outcome Compared and predicted Utrecht region in Rheumatoid Arthritis (BiOCURA) cohort. Eight hospitals of the Society for Rheumatology Research Utrecht included patients eligible for biological treatment, which were followed up to one year after start of this treatment. A univariate preselection (p<0.2) was performed to enter variables in the multivariable cox-regression model with backward selection (p<0.1). To develop a quick tool for clinical practice, the linear predictor was simplified by adjusting the coefficients to usable scores. Results: After one year of follow-up of 192 TNFi patients, 75 (39%) discontinued treatment, because of inefficacy (64%) or side-effects (33%). Discontinuation was predicted by a combination of female gender (HR=2.1, p=0.02), currently smoking (HR=1.8, p=0.03), RF positivity (HR=0.67, p=0.10), high VAS-GH score (HR=1.02/mm, p<0.01) and higher number of previous biological DMARDs (HR=1.5/biological, p<0.01). A simplified score for use in clinical practice was made (see table). For each of the five variables a patient scores points. The sum of these scores can determine if there is an absolute chance of discontinuation within the first year of 67% (score >7.00) or 83% (score >7.75). Analyses of the high versus low risk patients revealed no different reasons for discontinuation (p=0.27 and 0.90 for different cut-offs respectively). Interestingly, the course over time of DAS28 and DAS28-inflammation and pain component in high risk (score >7.00) versus low risk patients, revealed differences that put discontinuing in a more subjective context: Although absolute DAS28 scores were higher over time for the high risk patients (0.27-0.75), these differences could be explained by a higher relative pain component of the DAS28, which was increased up to 1.3 fold three months after start (p<0.01). Conclusions: TNFi discontinuation within the first year of use in an observational cohort can be predicted by a simple prediction score. The reported pain by patients is probably an underestimated factor in the clinical decision of discontinuation. To investigate if these findings are reproducible, validation will be performed at short notice when 100 subsequent TNFi treated patients are included in the BiOCURA cohort (currently n=75). Disclosure of Interest: None declared