Personalizing treatment targets in rheumatoid arthritis by using a simple prediction model.

OBJECTIVE

To develop a personalized treatment target approach in patients with rheumatoid arthritis (RA) based on baseline risk factors for joint damage progression in combination with disease activity over time.

METHODS

Data were used from the Nijmegen early RA cohort. Presence or absence of anticyclic citrullinated peptide antibodies (anti-CCP), high erythrocyte sedimentation rate, and erosions were translated into 4 risk profiles: 0, 1, 2, and 3. Joint damage progression was assessed with the Ratingen score, and disease activity with the original Disease Activity Score (DAS) over 3 years. The probability for joint damage progression was calculated for each risk profile and each DAS category using logistic regression models. The probabilities were translated into personalized disease activity treatment targets.

RESULTS

More risk factors at baseline as well as a higher DAS level resulted in a higher probability for joint damage progression in a dose-dependent way. Low DAS corresponded with a probability of 0.0, 0.08, 0.20, and 0.58 in patients with 0, 1, 2, and 3 risk factors, respectively. Moderate DAS corresponded with a probability of 0.06 in patients with 0 risk factors and 0.35 with 1 risk factor. High DAS resulted in a probability of 0.50 with no risk factors present at baseline.

CONCLUSION

Presence of anti-CCP, acute-phase response, and erosions at baseline can be used to set individual treatment targets in RA. In patients without these risk factors, a moderate DAS as a target is sufficient, while for patients with all 3 risk factors, a low DAS is not strict enough to limit the risk for joint damage.