How to diagnose rheumatoid arthritis early: A prediction model for persistent (erosive) arthritis

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Abstract
Objective To develop a clinical model for the prediction, at the first visit, of 3 forms of arthritis outcome: self‐limiting, persistent nonerosive, and persistent erosive arthritis. Methods A standardized diagnostic evaluation was performed on 524 consecutive, newly referred patients with early arthritis. Potentially diagnostic determinants obtained at the first visit from the patient's history, physical examination, and blood and imaging testing were entered in a logistic regression analysis. Arthritis outcome was recorded at 2 years' followup. The discriminative ability of the model was expressed as a receiver operating characteristic (ROC) area under the curve (AUC). Results The developed prediction model consisted of 7 variables: symptom duration at first visit, morning stiffness for ≥1 hour, arthritis in ≥3 joints, bilateral compression pain in the metatarsophalangeal joints, rheumatoid factor positivity, anti–cyclic citrullinated peptide antibody positivity, and the presence of erosions (hands/feet). Application of the model to an individual patient resulted in 3 clinically relevant predictive values: one for self‐limiting arthritis, one for persistent nonerosive arthritis, and one for persistent erosive arthritis. The ROC AUC of the model was 0.84 (SE 0.02) for discrimination between self‐limiting and persistent arthritis, and 0.91 (SE 0.02) for discrimination between persistent nonerosive and persistent erosive arthritis, whereas the discriminative ability of the American College of Rheumatology 1987 classification criteria for rheumatoid arthritis was significantly lower, with ROC AUC values of 0.78 (SE 0.02) and 0.79 (SE 0.03), respectively. Conclusion A clinical prediction model was developed with an excellent ability to discriminate, at the first visit, between 3 forms of arthritis outcome. Validation in other early arthritis clinics is necessary.