title: Remission in early rheumatoid arthritis: predicting treatment response.
abstract: OBJECTIVE
  Optimizing therapeutic strategies to induce remission requires an understanding of the initial features predicting remission. Currently no suitable model exists. We aim to develop a remission score using predictors of remission in early rheumatoid arthritis (RA).
  
  
  METHODS
  We used a dataset from a UK randomized controlled trial that evaluated intensive treatment with conventional combination therapy, to develop a predictive model for 24-month remission. We studied 378 patients in the trial who received 24 months' treatment. Our model was validated using data from a UK observational cohort (Early RA Network, ERAN). A group of 194 patients was followed for 24 months. Remission was defined as 28-joint Disease Activity Score < 2.6. Logistic regression models were used to estimate the associations between remission and potential baseline predictors.
  
  
  RESULTS
  Multivariate logistic regression analyses showed age, sex, and tender joint count (TJC) were independently associated with 24-month remission. The multivariate remission score developed using the trial data correctly classified 80% of patients. These findings were replicated using ERAN. The remission score has high specificity (98%) but low sensitivity (13%). Combining data from the trial and ERAN, we also developed a simplified remission score that showed that younger men with a TJC of 5 or lower were most likely to achieve 24-month remission. Remission was least likely in older women with high TJC. Rheumatoid factor, rheumatoid nodules, and radiographic damage did not predict remission.
  
  
  CONCLUSIONS
  Remission can be predicted using a score based on age, sex, and TJC. The score is relevant in clinical trial and routine practice settings.
authors.affiliation: Department of Rheumatology, King's College London School of Medicine, Weston Education Centre, King's College London, London SE5 9RS, UK. margaret.ma@nhs.net
authors.forename: Margaret H Y
authors.initials: MH
authors.lastname: Ma
authors.forename: Fowzia
authors.initials: F
authors.lastname: Ibrahim
authors.forename: David
authors.initials: D
authors.lastname: Walker
authors.forename: Andrew
authors.initials: A
authors.lastname: Hassell
authors.forename: Ernest H
authors.initials: EH
authors.lastname: Choy
authors.forename: Patrick D W
authors.initials: PD
authors.lastname: Kiely
authors.forename: Richard
authors.initials: R
authors.lastname: Williams
authors.forename: David A
authors.initials: DA
authors.lastname: Walsh
authors.forename: Adam
authors.initials: A
authors.lastname: Young
authors.forename: David L
authors.initials: DL
authors.lastname: Scott
mesh_terms: D000367:Age Factors; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy* / Q000503:physiopathology*; D004185:Disability Evaluation; D004359:Drug Therapy, Combination; D005260:Female; D006801:Humans; D007596:Joints / Q000503:physiopathology*; D016015:Logistic Models; D008297:Male; D008727:Methotrexate / Q000627:therapeutic use*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012074:Remission Induction; D012680:Sensitivity and Specificity; D012737:Sex Factors; D016896:Treatment Outcome; D006113:United Kingdom
chemical_list: D018501:Antirheumatic Agents; D008727:Methotrexate