title: A matrix risk model for the prediction of rapid radiographic progression in patients with rheumatoid arthritis receiving different dynamic treatment strategies: post hoc analyses from the BeSt study.
abstract: OBJECTIVE
  To develop a matrix model for the prediction of rapid radiographic progression (RRP) in subpopulations of patients with recent-onset rheumatoid arthritis (RA) receiving different dynamic treatment strategies.
  
  
  METHODS
  Data from 465 patients with recent-onset RA randomised to receive initial monotherapy or combination therapy were used. Predictors for RRP (increase in Sharp-van der Heijde score > or =5 after 1 year) were identified by multivariate logistic regression analysis. For subpopulations, the estimated risk of RRP per treatment group and the number needed to treat (NNT) were visualised in a matrix.
  
  
  RESULTS
  The presence of autoantibodies, baseline C-reactive protein (CRP) level, erosion score and treatment group were significant independent predictors of RRP in the matrix. Combination therapy was associated with a markedly reduced risk of RRP. The positive and negative predictive values of the matrix were 62% and 91%, respectively. The NNT with initial combination therapy to prevent one patient from RRP with monotherapy was in the range 2-3, 3-7 and 7-25 for patients with a high, intermediate and low predicted risk, respectively.
  
  
  CONCLUSIONS
  The matrix model visualises the risk of RRP for subpopulations of patients with recent-onset RA if treated dynamically with initial monotherapy or combination therapy. Rheumatologists might use the matrix for weighing their initial treatment choice.
authors.affiliation: Department of Rheumatology C1-R, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands. k.visser@lumc.nl
authors.forename: K
authors.initials: K
authors.lastname: Visser
authors.forename: Y P M
authors.initials: YP
authors.lastname: Goekoop-Ruiterman
authors.forename: J K
authors.initials: JK
authors.lastname: de Vries-Bouwstra
authors.forename: H K
authors.initials: HK
authors.lastname: Ronday
authors.forename: P E H
authors.initials: PE
authors.lastname: Seys
authors.forename: P J S M
authors.initials: PJ
authors.lastname: Kerstens
authors.forename: T W J
authors.initials: TW
authors.lastname: Huizinga
authors.forename: B A C
authors.initials: BA
authors.lastname: Dijkmans
authors.forename: C F
authors.initials: CF
authors.lastname: Allaart
mesh_terms: D000911:Antibodies, Monoclonal / Q000627:therapeutic use; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000000981:diagnostic imaging* / Q000188:drug therapy* / Q000276:immunology; D001323:Autoantibodies / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism; D018450:Disease Progression; D004359:Drug Therapy, Combination; D004812:Epidemiologic Methods; D005260:Female; D005938:Glucocorticoids / Q000627:therapeutic use; D006801:Humans; D000069285:Infliximab; D008297:Male; D008727:Methotrexate / Q000627:therapeutic use; D008875:Middle Aged; D010456:Peptides, Cyclic / Q000276:immunology; D011241:Prednisone / Q000627:therapeutic use; D011859:Radiography; D012217:Rheumatoid Factor / Q000097:blood
chemical_list: D000911:Antibodies, Monoclonal; D018501:Antirheumatic Agents; D001323:Autoantibodies; D005938:Glucocorticoids; D010456:Peptides, Cyclic; C487763:cyclic citrullinated peptide; D002097:C-Reactive Protein; D012217:Rheumatoid Factor; D000069285:Infliximab; D011241:Prednisone; D008727:Methotrexate