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FRI0171 Evaluating Treatment Efficacy in Rheumatoid Arthritis: Interrelation Disease Activity – Functional Status – Utility
  1. C. Mogosan1,
  2. C. Codreanu1,
  3. L. Enache1,
  4. M. Parvu2,
  5. S. Rednic3,
  6. R. Ionescu4
  1. 1Rheumatology, Clinical Center of Rheumatic Diseases
  2. 2Rheumatology, Colentina Hospital, Bucharest
  3. 3Rheumatology, County Hospital, Cluj Napoca
  4. 4Rheumatology, Sfanta Maria Hospital, Bucharest, Romania


Background Rheumatoid Arthritis (RA) treatment targets remission or low disease activity. Patient Registries offer real-world data about effectiveness on long term. Comparing different tools for assessing effectiveness (disease activity, functional status and utility) could provide a more comprehensive approach on the term “achieve and maintain” the therapeutic goal, as a measure of efficacy.

Objectives To evaluate interrelations of disease activity – functional status – utility in a group of RA patients, treated with tumor necrosis factor alpha (TNF) antagonists and anti CD20 molecule, as well as any factor that could influence it.

Methods Cross-sectional study carried out over a cohort of RA patients in Romania. Data was gathered from the Romanian Registry of Rheumatic Diseases and included only RA patients treated with biologics, for who were available all three variables under study: EQ5D (for utility), HAQ score (for functional status), and DAS28 and SDAI (for disease activity). The interrelations of the efficacy parameters were analyzed using correlation tests, T test, ANOVA.

Results The study cohort included 777 RA patients, mean age 58.72 (±12.37) yrs, 84.4% women, mean RA duration 14.08 (±8.33) yrs, 77.1% retired, treated with etanercept (30.6%), adalimumab (22.7%), infliximab (original 6.3%, biosimilars 0.7%), rituximab (26.3%) with a mean treatment duration 174.90 (±143.40) weeks; 27.3% used steroids (20.2% with <7.5mg daily); mean current DAS28 score = 3.66 (±1.52), mean delta DAS28 = 0.13 (±1.28), mean SDAI = 14.27 (±13.79), mean HAQ score = 1.14 (±0.64), mean EQ5D = 0.61±0.31. There was a strong positive correlation between HAQ score and DAS28, as well as SDAI: r=0.5 (p<0.001) and a negative association between HAQ and EQ5D: r= -0.6 (p<0.001). Utility score showed a strong negative association with DAS28 and with SDAI: r= -0.7 (p<0.001), whereas disease activity dynamics (delta DAS28 for the last 6 months) had a poor negative association with EQ5D: r= -0.2 (p<0.01), but not with HAQ. Patient global assessment (PGA) on disease activity had a significant association both with HAQ (r=0.6, p<0.001) and EQ5D (r= -0.6, p<0.01). Analyzing distribution of EQ5D and HAQ in DAS28 categories, there was a significant difference between categories (p<0.001); for EQ5D-DAS28: <2.6 = 0.81 (±0.19), 2.6–3.2 = 0.68 (±0.17), 3.2–5.1 = 0.63 (±0.18), >5.1 = 0.16 (±.38); for HAQ-DAS28: <2.6 = 0.73 (±0.57), 2.6–3.2 = 0.98 (±0.5), 3.2–5.1 = 1.25 (±0.52), >5.1 = 1.76 (±0.56). Steroids use belongs to more active disease; doses >7.5 mg/day represent a cutoff for patients with severe disease activity [HDA]. Disease duration had not any influence on EQ5D, HAQ or DAS28, as well as all other factors (age, sex, residency, education, work status).

Conclusions Disease activity influences the patient well-being and functionality. Considering the dynamics of disease activity over time and the fluctuating values of utility and HAQ score within DAS28 categories, analyzing treatment efficacy from a combined perspective may be helpful in evaluating the time trends on achieving and maintaining the therapeutic goals.

Disclosure of Interest None declared

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