Division of Rheumatology, Allergy and Immunology, University of California, San Diego, USA
Correspondence to: A. Kavanaugh, Division of Rheumatology, Allergy and Immunology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 920930943, USA. E-mail: Akavanaugh{at}ucsd.edu
SIR, We read with great interest the recent review by Scott et al. [1] concerning the routine use of tender and swollen joint counts in clinical practice and the regular assessment of rheumatoid inflammation. The recent introduction of biological agents has created substantial interest in methods to assess the effectiveness of therapy because of their unique properties, such as quick response rates after institution of therapy. However, rheumatologists face the paradigm of how to best evaluate disease activity in clinical setting. It has not yet been established which disease activity measures are ideal for use in the clinical setting.
At our hospital rheumatology clinic, the medical charts of 21 patients with either rheumatoid arthritis (n = 17) or psoriatic arthritis (n = 4), who had been recently begun tumour necrosis factor (TNF) inhibitor therapy, were reviewed over a 4-week period in order to evaluate how disease activity measurements correlated in assessing the response to anti-TNF therapy. Data collected at each visit prior to and following initiation of TNF therapy included tender and swollen joint scores, erythrocyte sedimentation rate (ESR) and/or C-reactive protein (CRP), Health Assessment Questionnaire (HAQ) (03), pain [10-cm visual analogue scale (VAS)], duration of morning stiffness, fatigue (10-cm VAS) and global disease severity (10-cm VAS). At baseline the mean HAQ score was 1.07, and the mean Disease Activity Score (DAS28) was 5.03.
Despite efforts to obtain complete data sets, a DAS28 could not be calculated both prior to and post treatment for 4 patients (19%), typically because of missing laboratory values. Of the 13 RA patients for whom pre- and post-treatment DAS28 scores were calculated, 9 were DAS responders (5 good and 4 moderate). Looking at the 4 DAS 28 non-responders, there was marked heterogeneity among other individual outcome measures (Table 1). Of note, among 3 of 4 DAS28 non-responders, there was typically substantial improvement in other measures. Among the DAS28 good responders, HAQ and fatigue score showed the highest heterogeneity (Table 2).
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Another clinically important factor is determining prognosis [2]. Indeed how accurately can these disease measures predict clinical outcome? The results from our small cohort show that there is a correlation between how patients respond to therapy and pre-treatment questionnaire responses. In particular, for every one-point increment in pre-treatment HAQ score, there was a 23% smaller improvement in pain score (P<0.045). This means that patients with more disability before commencing therapy might achieve less improvement in pain compared with those patients with no disability. There was no other correlation found between pre-treatment scores and post-treatment improvement.
Our findings suggest that although composite disease activity measures like DAS28 can provide accurate assessment in both research trials as well as in clinic settings, they do not necessarily correlate with changes in individual disease activity parameters. There is marked heterogeneity among all these measures. Also, in an actual clinical setting complete data may not always be collected. Therefore, it would be prudent for clinicians to try to collect as many various data as possible until the optimal parameters for assessing response to therapy and outcome can be determined in clinical setting.
The authors have declared no conflicts of interest.
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