Background/Purpose: The University of North Carolina (UNC) Hospitals Rheumatology Clinic is utilizing the Clinical Disease Activity Index (CDAI) in patients with rheumatoid arthritis (RA) per the American College of Rheumatology (ACR) recommendation to guide therapeutic decisions in a treat-to-target approach. Through quality initiatives in clinic, approximately 70% of RA encounters now capture CDAI. This study was designed to understand covariables impacting CDAI over time and treatment actions, as well as determine generalizable applicability in clinical practice.

Methods: This was a single-center, retrospective cohort study evaluating the use of CDAI to monitor disease activity and treatment response for adult patients (≥18 years) with RA, defined by treating rheumatologist from 10/01/2022 to 09/30/2023. Patients were included if they had three documented CDAI scores within the study period. Analyses included associations between CDAI with sex, race, insurance, income, steroid use, steroid sparing agents (conventional and targeted DMARDs and/or biologics) over time. The utility of the CDAI was examined by assessing whether provider’s treatment actions were associated with CDAI scores vs other reasons such as patient’s preference, medication side effect profile, or insurance coverage. Mixed linear (random intercept and linear time, other covariables as fixed effects, and unstructured covariance) and population-averaged logistic regression, using generalized estimating equations, were used to produce betas or odds ratios (OR), respectively, and 95% confidence intervals (CI) for these associations.

Results: A total of 234 patients had three CDAI scores within 12 months. Mean baseline CDAI score was 15.5 ± 12. The mean time from visit 1 to visit 2 and 3 was 15.1 weeks and 31.8 weeks, respectively (Table 1). CDAI score significantly decreased over time, on average 2.9 units (95% CI -4.0, -1.8) after 15 weeks and 3.5 units (95% CI -4.8, -2.1) after 30 weeks from baseline. CDAI score was significantly higher for those with no insurance vs private insurance by 3.9 units (95% CI -0.0, 7.8). Patients with Medicare or other government insurances (vs private) had lower odds of escalation or switch in therapy, OR 0.57 (95% CI 0.33, 0.98) and OR 0.50 (95% CI 0.28, 0.90), respectively (Table 2). Patients with moderate or high CDAI (compared to low/remission) had higher odds of escalation or switch in therapy and lower odds of no change or taper therapy (Figure 1, Table 2). Treatment actions guided by CDAI scores had higher odds of escalation or switch in therapy, OR 2.83 (95% CI 1.47, 5.46) and lower odds for tapering therapy, OR 0.15 (95% CI 0.06, 0.33) compared to other reasons (Table 2).

Conclusion: Within the UNC Rheumatology Clinic, treatment changes were guided by CDAI scores. Other factors (e.g., access to insurance or insurance types, income, and race/ethnicity) may also play a role in treatment decisions and degree of disease activity. Therefore, other clinic resources may need to be utilized to bridge gaps between these disparities to provide equitable care to all patients. Further quality improvement studies are needed to see if treatment decisions and outcomes are different in patients with RA who are seen without a CDAI score.

Table 1 – Descriptive Statistics for RA CDAI Utilization

Figure 1 – Percent of treatment action by CDAI score group

Table 2 – Adjusted associations between covariables and treatment action over time, RA CDAI

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/real-world-implication-of-clinical-disease-activity-index-cdai-utilization-on-treatment-decisions-and-clinical-outcomes-in-rheumatoid-arthritis/