Objectives: To predict the long-term outcome of rheumatoid arthritis (RA) with respect to radiographic damage, disability, and disease course using baseline variables, and to construct decision trees identifying patients on an individual level at the extremes of the outcome spectrum of these 3 dimensions.
Methods: The 12-year outcome of 112 female RA patients from a prospective inception cohort was assessed by measuring the tertiles of radiographic damage (measured by the modified Sharp/van der Heijde method, SHS), disability (measured by the Health Assessment Questionnaire, HAQ), and severe disease course as defined by patients with either the 33% highest cumulative disease activity (area under the curve of all observed disease activity scores) or the highest tertile of radiographic damage. Patients 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 patients 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 patients had a severe disease course. Mild and severe radiographic damage 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 severe disease course 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 RA is possible and can be done using widely available baseline parameters.