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2026
Journal Article
Title
Unmet Needs in Treatment Escalation for Chronic Spontaneous Urticaria: Findings From the CURE Registry
Abstract
Background: Many patients with chronic spontaneous urticaria (CSU) remain symptomatic despite receiving second-generation H1-antihistamines (sgH1-AH). This data analysis from the Chronic Urticaria Registry (CURE) aimed to describe treatment patterns and identify unmet needs in real-world practice. Methods: CURE is an international, prospective registry of patients with chronic urticaria. Treatment responses were categorized as Urticaria Control Test (UCT) changes from baseline (BL) to 6-month follow-up (FU). Complete response was defined as UCT = 16 with a ≥ 3-point increase. Results: Data were available from 3995 adult patients with CSU at BL and 1288 at FU with evaluable UCT. After treatment escalation from BL to FU, 5.3% (no treatment to licensed-dose sgH1-AH), 6.0% (licensed-dose sgH1-AH to up-dosed sgH1-AH), and 28.4% (any dose sgH1-AH to omalizumab) achieved complete response. Factors associated with a lower probability of treatment escalation at FU were UCT ≥ 12 and omalizumab treatment at BL (both p < 0.0001). About one-third (28.6%) of patients clinically eligible for escalation at BL (UCT < 12) did not receive step-up treatment (18.0%) or were even stepped down (10.6%) and remained poorly controlled at FU. Factors associated with lack of escalation in this group included younger age (p = 0.014), shorter disease duration (p = 0.071), presence of wheals and angioedema (p = 0.002), better quality of life (p = 0.001), and treatment with up-dosed sgH1-AH (p = 0.031). Conclusion: Appropriate treatment escalation improves CSU control, although only about a quarter of patients achieve a complete response, indicating the need for novel treatments. Many patients with poorly controlled CSU do not receive guideline-recommended treatment escalation and remain symptomatic on their current treatments, which deserves further attention.
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Open Access
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Rights
CC BY 4.0: Creative Commons Attribution
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Language
English