title: A simple algorithm to predict the development of radiological erosions in patients with early rheumatoid arthritis: prospective cohort study.
abstract: OBJECTIVE
  To produce a practical algorithm to predict which patients with early rheumatoid arthritis will develop radiological erosions.
  
  
  METHODS
  Primary care based prospective cohort study.
  
  
  METHODS
  All general practices in the Norwich Health Authority, Norfolk.
  
  
  METHODS
  175 patients notified to the Norfolk Arthritis Register were visited by a metrologist soon after they had presented to their general practitioners with inflammatory polyarthritis, and again after a further 12 months. All the patients satisfied the American Rheumatism Association's 1987 criteria for rheumatoid arthritis and were seen by a metrologist within six months of the onset of symptoms. The study population was randomly split into a prediction sample (n = 105) for generating the algorithm and a validation sample (n = 70) for testing it.
  
  
  METHODS
  Predictor variables measured at baseline included rheumatoid factor status, swelling of specific joint areas, duration of morning stiffness, nodules, disability score, age, sex, and disease duration when the patient first presented. The outcome variable was the presence of radiological erosions in the hands or feet, or both, after 12 months.
  
  
  RESULTS
  A simple algorithm based on a combination of three variables--a positive rheumatoid factor test, swelling of at least two large joints, and a disease duration of more than three months--was best able to predict erosions. When the accuracy of this algorithm was tested with the validation sample, the erosion status of 79% of patients was predicted correctly.
  
  
  CONCLUSIONS
  A simple algorithm based on three easily measured items of information can predict which patients are at high risk and which are at low risk of developing radiological erosions.
authors.affiliation: Arthritis and Rheumatism Council Epidemiology Research Unit, University of Manchester Medical School.
authors.forename: P
authors.initials: P
authors.lastname: Brennan
authors.forename: B
authors.initials: B
authors.lastname: Harrison
authors.forename: E
authors.initials: E
authors.lastname: Barrett
authors.forename: K
authors.initials: K
authors.lastname: Chakravarty
authors.forename: D
authors.initials: D
authors.lastname: Scott
authors.forename: A
authors.initials: A
authors.lastname: Silman
authors.forename: D
authors.initials: D
authors.lastname: Symmons
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000473:pathology*; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D011446:Prospective Studies; D011859:Radiography; D012307:Risk Factors; D012680:Sensitivity and Specificity