OBJECTIVES: To predict the long-term outcome of @entity309 ( @entity309 ) with respect to @entity16 , disability, and disease course using baseline variables, and to construct decision trees identifying @entity1 on an individual level at the extremes of the outcome spectrum of these 3 dimensions. METHODS: The 12-year outcome of 112 female @entity309 @entity1 from a prospective inception cohort was assessed by measuring the tertiles of @entity16 (measured by the modified Sharp/van der Heijde method, SHS), disability (measured by the Health Assessment Questionnaire, HAQ), and @entity66 as defined by @entity1 with either the 33% highest cumulative disease activity (area under the curve of all observed disease activity scores) or the highest tertile of @entity16 . @entity1 in the lowest (mild) and highest tertile (severe) of each outcome measure were identified. All baseline parameters known to be associated with each outcome (demographic and socioeconomic parameters; disease duration; disease activity measures; laboratory measures including rheumatoid factor, HLA typing, percentage agalactosyl IgG, functional and radiographic measures) were entered into cross-validated stepwise logistic regression models to find the best predictive combination of baseline parameters for each of the outcomes. Using the results of the logistic regression models, simple decision trees were constructed to categorize @entity1 at an individual level in a particular prognostic group. RESULTS: After 12 years, the lowest and highest tertiles were, respectively, 42.3 and 189 for the SHS and 0.37 and 1.25 for the HAQ. Fifty-five @entity1 had a @entity66 . Mild and severe @entity16 could be predicted with an accuracy of 90% and 85%, respectively. Mild and severe HAQ could be predicted with an accuracy of 90% and 84%, respectively, and @entity66 with an accuracy of 81%. The baseline variables found to be predictive of all 3 outcome measures were very similar and consisted of combinations of the following baseline parameters: swollen joint count (SJC), Ritchie score, rheumatoid factor (RF), the presence of erosions, and the HAQ score. Additional knowledge of the HLA typing hardly improved the accuracy of the prediction. To predict outcome at the individual level, simple decision trees were constructed using the RF, HAQ, SJC, and presence of erosions at baseline. CONCLUSION: The present study shows that prediction of outcome in long-term @entity309 is possible and can be done using widely available baseline parameters.

Long-term outcome in @entity309 : a simple algorithm of baseline parameters can predict XXXX , disability, and disease course at 12-year followup.

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients'] @entity16 :: ('MESH:D009422', 'Disease') :: ['radiographic damage'] @entity309 :: ('MESH:D001172', 'Disease') :: ['rheumatoid arthritis', 'RA'] @entity66 :: ('MESH:D004194', 'Disease') :: ['severe disease course']
@entity16 :: (MESH:D009422,Disease) :: ['radiographic damage']
