Validation of a prediction rule for disease outcome in patients with recent-onset undifferentiated arthritis: Moving toward individualized treatment decision-making

Authors


Abstract

Objective

The decision to start disease-modifying antirheumatic drugs in patients with recent-onset undifferentiated arthritis (UA) is complicated by a varied natural disease course in which the disease in one-third of patients progresses to rheumatoid arthritis (RA), whereas 40–50% of patients experience spontaneous remission. Recently, a prediction rule was developed to estimate the chance of progression to RA in individual patients presenting with UA. This study investigates the accuracy of this prediction rule in independent cohorts of patients with UA.

Methods

In 3 cohorts of patients with recent-onset UA, from the UK, Germany, and The Netherlands, the prediction score and the corresponding chance of developing RA were calculated. These data were compared with the observed disease outcome after ≥1 year of followup. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated and the overall discriminative ability of the prediction rule was assessed using area under the receiver operating characteristic curves (AUCs).

Results

Since data on the severity of morning stiffness were not available in all validation cohorts, the prediction rule was rederived with the duration of morning stiffness as a substitute. The AUC for this rule was 0.88 (SEM 0.015). For each validation cohort, the AUC was 0.83 (SEM 0.041), 0.82 (SEM 0.037), and 0.95 (SEM 0.031) in the British, German, and Dutch cohorts, respectively. The NPV (for a prediction score ≤6) in these 3 cohorts was 83%, 83%, and 86%, respectively; the PPV (for a prediction score ≥8) was 100%, 93%, and 100%, respectively.

Conclusion

The recently derived prediction rule, when applied to 3 independent cohorts of patients with UA, has an excellent discriminative ability for assessing the likelihood of progression to RA. Application of this rule will allow individualized treatment decision-making for patients with UA.

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