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THU0168 How Low Should You Go? Towards Personalized Treatment Targets for Disease Activity in RA
  1. Y. M. R. De Punder1,
  2. T. L. Jansen1,
  3. A. E. van Ede1,
  4. A. A. den Broeder2,
  5. P. L. van Riel1,
  6. J. Fransen1
  1. 1Rheumatology, Radboud University Nijmegen Medical Centre
  2. 2Rheumatology, Sint Maartenskliniek, Nijmegen, Netherlands

Abstract

Background Prevention of joint damage is a main goal of treatment in Rheumatoid Arthritis (RA). However, not all patients have the same risk for joint damage during the course of disease. This is partly dependent on the level of disease activity over time, but also on three main prognostic factors at baseline: presence of anti-CCP, elevated ESR and presence of erosions. Therefore, in establishing a treatment target to prevent joint damage, the presence of these risk factors may be taken into account. A differentiation can be made in the one-size-fits-all treatment target of clinical remission and individualized treatment targets can be defined.

Objectives To define DAS treatment targets for the prevention of joint damage for four different baseline risk profiles, based on the presence of anti-CCP, increased ESR and erosions.

Methods Data were used from year 0 to 3 of patients of the Nijmegen Early RA cohort. Presence or absence of the three main prognostic factors anti-CCP, high ESR and erosions at baseline was combined in an individual risk profile, with a risk score of 0 to 3, representing the number of risk factors. Joint damage progression was assessed with the Ratingen score at 0 and 3 years and the Disease Activity Score (DAS) was calculated every 3 months. Logistic regression models were used for analyses. Based on the probability for joint damage progression after 3 years, treatment targets were defined for each risk profile. A probability cut point of <=0.30 was considered an acceptable risk for progressive joint damage.

Results An increasing number of risk factors, as well as an increasing level of DAS over time resulted in a higher probability for joint damage progression after three years. The combination of these two associations showed that a higher baseline risk score corresponded with a lower acceptable level of disease activity for the same probability for joint damage progression. Table 1 shows the treatment targets for each risk profile, based on the cut point for acceptable probability of <=0.30.

Conclusions Personalized risk profiles can be translated to personalized DAS treatment targets. Personalized risk profiles can be defined, based on presence of the three main baseline prognostic factors for joint damage in RA: anti-CCP, high ESR and erosions. The probability for joint damage is higher for patients with a higher risk profile. The higher the risk profile, the lower the acceptable level of disease activity to prevent joint damage.

Disclosure of Interest None Declared

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