title: Predicting the outcome of ankylosing spondylitis therapy.
abstract: OBJECTIVE
  To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS).
  
  
  METHODS
  ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model.
  
  
  RESULTS
  Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population.
  
  
  CONCLUSIONS
  Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.
authors.affiliation: Schering-Plough Corporation, Kenilworth, New Jersey, USA. nathan.vastesaeger@merck.com
authors.forename: Nathan
authors.initials: N
authors.lastname: Vastesaeger
authors.forename: Désirée
authors.initials: D
authors.lastname: van der Heijde
authors.forename: Robert D
authors.initials: RD
authors.lastname: Inman
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Wang
authors.forename: Atul
authors.initials: A
authors.lastname: Deodhar
authors.forename: Benjamin
authors.initials: B
authors.lastname: Hsu
authors.forename: Mahboob U
authors.initials: MU
authors.lastname: Rahman
authors.forename: Ben
authors.initials: B
authors.lastname: Dijkmans
authors.forename: Piet
authors.initials: P
authors.lastname: Geusens
authors.forename: Bert
authors.initials: B
authors.lastname: Vander Cruyssen
authors.forename: Eduardo
authors.initials: E
authors.lastname: Collantes
authors.forename: Joachim
authors.initials: J
authors.lastname: Sieper
authors.forename: Jürgen
authors.initials: J
authors.lastname: Braun
mesh_terms: D000328:Adult; D000367:Age Factors; D000465:Algorithms; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism; D004812:Epidemiologic Methods; D005260:Female; D020022:Genetic Predisposition to Disease; D005838:Genotype; D015796:HLA-B27 Antigen / Q000235:genetics; D006801:Humans; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D011379:Prognosis; D013167:Spondylitis, Ankylosing / Q000097:blood / Q000188:drug therapy* / Q000235:genetics; D016896:Treatment Outcome; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors*
chemical_list: D018501:Antirheumatic Agents; D015415:Biomarkers; D015796:HLA-B27 Antigen; D014409:Tumor Necrosis Factor-alpha; D002097:C-Reactive Protein