Comparison of Real-World On-Label Treatment Persistence in Patients with Psoriatic Arthritis Receiving Guselkumab Versus Subcutaneous Tumor Necrosis Factor Inhibitors.
Jessica A Walsh, Iris Lin, Ruizhi Zhao, Natalie J Shiff, Laura Morrison, Bruno Emond, Louise H Yu, Samuel Schwartzbein, Patrick Lefebvre, Dominic Pilon, Soumya D Chakravarty, Philip Mease
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引用次数: 0
Abstract
Background: Treatment persistence among patients with psoriatic arthritis (PsA) is essential for achieving optimal treatment outcomes. Guselkumab, a fully human interleukin-23p19-subunit inhibitor, was approved by the United States (US) Food and Drug Administration for the treatment of active PsA in July 2020, with a dosing regimen of 100 mg at week 0, week 4, then every 8 weeks. In the Phase 3 DISCOVER-1 and DISCOVER-2 studies of patients with active PsA, 94% of guselkumab-randomized patients completed treatment through 1 year and 90% did so through 2 years (DISCOVER-2). Real-world evidence is needed to compare treatment persistence while following US prescribing guidelines (i.e., on-label persistence) for guselkumab versus subcutaneous (SC) tumor necrosis factor inhibitors (TNFis).
Methods: Adults with PsA receiving guselkumab or their first SC TNFi (i.e., adalimumab, certolizumab pegol, etanercept, or golimumab) between 14 July 2020 and 31 March 2022 were identified in the IQVIA PharMetrics® Plus database (first claim defined the treatment start date [index date]). Baseline characteristics and biologic use (biologic-naïve/biologic-experienced) were assessed during the 12-month period preceding the index date. Baseline characteristics were balanced between cohorts using propensity-score weighting based on the standardized mortality ratio approach. The follow-up period spanned from the index date until the earlier of the end of continuous insurance eligibility or end of data availability. On-label persistence, defined as the absence of treatment discontinuation (based on a gap of 112 days for guselkumab or 56 days for SC TNFi) or any dose escalation/reduction during follow-up, was assessed in the weighted treatment cohorts using Kaplan-Meier (KM) curves. A Cox proportional hazards model, further adjusted for baseline biologic use, was used to compare on-label persistence between the weighted cohorts.
Results: The guselkumab cohort included 526 patients (mean age 49.8 years; 61.2% female) and the SC TNFi cohort included 1953 patients (mean age: 48.5 years; 60.2% female). After weighting, baseline characteristics were well balanced with a mean follow-up of 12.3-12.4 months across cohorts; 51.5% of patients in the guselkumab cohort and 16.7% in the SC TNFi cohort received biologics in the 12-month baseline period. Respective rates of treatment persistence at 3, 6, 9, and 12 months were 91.2%, 84.1%, 75.9%, and 71.5% for the guselkumab cohort versus 77.3%, 61.6%, 50.0%, and 43.7% for the SC TNFi cohort (all log-rank p < 0.001). At 12 months, patients in the guselkumab cohort were 3.0 times more likely than patients in the SC TNFi cohort to remain persistent on treatment (p < 0.001). Median time to discontinuation was not reached for the guselkumab cohort and was 8.9 months for the SC TNFi cohort.
Conclusion: This real-world study employing US commercial health-plan claims data to assess on-label treatment persistence in PsA demonstrated that, at 12 months, guselkumab was associated with a 3 times greater likelihood of persistence compared with SC TNFi.
期刊介绍:
Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.