Lowering expectations: glucocorticoid tapering among Veterans with rheumatoid arthritis achieving low disease activity on stable biologic therapy

Abstract: Objective: In the Steroid EliMination In Rheumatoid Arthritis (SEMIRA) trial, 65% of patients with rheumatoid arthritis (RA) in low disease activity (LDA) on stable biologic therapy successfully tapered glucocorticoids. We aimed to evaluate real-world rates of glucocorticoid tapering among similar patients in the Veterans Affairs Rheumatoid Arthritis registry. Methods: Within a multicenter, prospective RA cohort, we used registry data and linked pharmacy claims from 2003 to 2021 to identify chronic prednisone users achieving LDA after initiating a new biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD). We defined the index date as first LDA occurring 60 to 180 days after b/tsDMARD initiation. The primary outcome of successful tapering, assessed at day 180 after LDA, required a 30-day averaged prednisone dose both less than or equal to 5mg/day and at least 50% lower than at the index date. The secondary outcome was discontinuation, defined as a prednisone dose of 0 mg/day at days 180 through 210. We used univariate statistics to compare patient characteristics by fulfillment of the primary outcome. Results: We evaluated 100 b/tsDMARD courses among 95 patients. Fifty-four courses resulted in successful tapering; 33 resulted in discontinuation. Positive rheumatoid factor, higher erythrocyte sedimentation rate, more background DMARDs, shorter time from b/tsDMARD initiation to LDA, and higher glucocorticoid dose 30 days before LDA were associated with greater likelihood of successful tapering. Conclusion: In a real-world RA cohort of chronic glucocorticoid users in LDA, half successfully tapered and a third discontinued prednisone within 6 months of initiating a new b/tsDMARD. Claims-based algorithms of glucocorticoid tapering and discontinuation may be useful to evaluate predictors of tapering in administrative data sets.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.