ABS0730 TREATMENT SEQUENCING AND LINE OF THERAPY FOR BIOLOGICS AND JAK INHIBITORS IN RHEUMATOID ARTHRITIS PATIENTS: IMPLICATIONS FOR DESIGN AND UPTAKE OF NEW DRUGS AND DIAGNOSTICS

Background: Patients with rheumatoid arthritis (RA) have numerous treatment choices, and new diagnostic tests to predict treatment response are being developed. However, some RA therapies and predictive biomarker tests may be restricted by health plans or payers for use in RA patients with specific treatment histories (e.g., biologic-naïve or minimally biologic-experienced). Objectives: Using a population-based RA inception cohort, we examined RA treatment sequences by line of therapy (LoT) and medication switching rates to estimate the size of eligible populations for novel RA therapies or predictive treatment response tests. Methods: We utilized an augmented health plan claims database linked to electronic health record (EHR) data from the Excellence Network in Rheumatology to Innovate Care and High-impact research (ENRICH), a national practice-based rheumatology network. Study inclusion criteria included ≥2 outpatient encounters with a diagnosis of RA from rheumatology providers occurring ≥30 days apart and within 365 days in patients age ≥18 years, ≥12 months of continuous coverage preceding the first RA diagnosis through the second RA diagnosis, and ≥1 conventional, biologic or targeted synthetic DMARD (b/tsDMARD) between the first RA diagnosis date and the end of the study. Patients with other inflammatory rheumatic diseases or use of b/tsDMARDs prior to the first RA diagnosis were excluded. Biologic and JAKi were identified using various coding systems including the Healthcare Common Procedure Coding System (HCPCS), National Drug Codes (NDC), prescription concept unique identifier (RXCUI) from the National Library of Medicine (NLM), and generic/brand name. We assessed the characteristics of the cohort by line of therapy, the rate of RA treatment switching overall and by rheumatology provider, time to switch, and the sequence of RA treatments used, stratified by line of therapy and shown as a Sankey plot. We also examined agreement between LoT as characterized in claims and EHR data compared to the gold standard of patient self-reported lifetime RA treatment history. Agreement was reported using weighted Cohen's Kappa(κ). Results: Among 37,656 newly-diagnosed RA patients, 59,557 new RA treatment initiations were identified (Table 1). The median [IQR] age of newly-diagnosed RA patients was 54.3 years [45.1, 61.4] at first b/tsDMARD initiation. Patients were mostly commercially insured (78.5%), White (68.5%, among those with available race), and had a high proportion of oral glucocorticoids utilization (68.1%) within 6 months of treatment initiation. Among b/tsDMARDs naïve patients, 36.5% of newly-diagnosed patients used their first LoT as monotherapy, and the prevalence of monotherapy use (e.g. no concomitant MTX) increased to almost 50% as patients received additional b/tsDMARDs. Most patients (85.2%) initiated biologics (predominantly TNFi); the remainder initiated JAKi (Figure 1). Of these, 40.2% overall used biologics/JAKi as monotherapy. The frequency of JAKi initiation peaked in 2019-2020 (18.3%) and declined to 13.7% in 2023-2024. The overall medication switching rate was 25.7 per 100 patient-years (22.7/100py in biologic-naïve patients, >30/100py in treatment-experienced patients), with substantial variability between prescribers (Figure 2). The time to switch was appreciably longer (p < 0.001) among b/tsDMARDs naïve patients compared to treatment-experienced patients but differed minimally with increasing failure of RA treatments. In both claims and EHR data, use of a 1-year minimum baseline that used all available prior data had much better agreement (claims: κ=0.68; EHR: κ=0.59) with patients' self-reported lifetime treatment history than a fixed 1-year baseline (claims: κ=0.32; EHR: κ=0.30). Conclusion: These findings provide valuable insights for stakeholders developing or marketing RA therapies or diagnostic tests, highlighting the size and characteristics of the eligible RA population in the U.S. and their treatment patterns by line of therapy. REFERENCES: NIL . Figure 1Treatment Switching by Line of Therapy (n=37,656 unique RA patients) Figure 2Variability in the Rate of RA Treatment Switches, by Prescriber (n=1,726 prescribers with at least 10 RA patients represented in the analysis*; n=36,406 initiations)*removing 8 prescribers who were outliers with switch rates >100 per 100 patient-years Acknowledgements: NIL . Disclosure of Interests: Jeffrey R Curtis AbbVie, Amgen, Aqtual, BMS, GSK, Janssen, Lilly, Moderna, Novartis, Pfizer, Sanofi, Scipher, Setpoint, TNacity Blue Ocean, UCB, AbbVie, Amgen, Aqtual, BMS, CorEvitas, GSK, Janssen, Lilly, Moderna, Novartis, Pfizer, Sanofi, Scipher, Setpoint, UCB, Shanette Daigle: None declared , Emily E Holladay: None declared , Yujie Su: None declared , Fenglong Xie: None declared , Signe Fransen Aqtual Inc., Gordon Lam Aqtual Inc., Robert Levin AbbVie, BMS, Sanofi, AbbVie, Amgen, Lilly, Pfizer, Sanofi, UCB, Gilead, Myriad Genetics, Shanmugapriya Reddy ABBVIE, Amgen, Pfizer, UCB, Sanofi, Novartis, BMS, Lilly, ABBVIE, Amgen, Pfizer, UCB, Sanofi, Novartis, BMS, Lilly, Jason Carlson Aqtual Inc., Amy Mudano: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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