The Urticaria Voices Study: Physicians’ Perspectives on the Real-World Patient Burden, Treatments, and Outcomes in Chronic Spontaneous Urticaria
Bibliographic record
Abstract
Chronic spontaneous urticaria (CSU) significantly impacts patients’ quality of life (QOL). Understanding physicians’ perspectives and treatment approaches for CSU is crucial for optimizing the outcomes. We describe the CSU management challenges and treatment perceptions reported by physicians in the Urticaria Voices study. This is a multinational cross-sectional online survey involving patients with CSU and CSU-treating physicians from seven countries (USA, Canada, UK, Germany, France, Italy, and Japan). The reported analyses assessed prescribing patterns, treatment satisfaction, and disease management challenges for physicians. Data were analyzed descriptively. Overall, 862 physicians (517 dermatologists; 345 allergists) participated in the study. Fifty-two percent perceived CSU as serious and 65% reported that CSU negatively impacts patients’ life, particularly mental well-being (mean [SD], 8.2 [1.7]; 10-point scale). Key challenges included treatment-related issues (approx. 66%) and diagnosis (39%). Globally, 56% of physicians adhered to guidelines, 19% followed therapeutic protocols, and approximately 30% did not adhere to any guideline. Physicians reported that 80% of patients were on H 1 -antihistamines (H1-AH; second-generation H1-AH [sgH1-AH], 57%; first-generation H1-AH, 23%), 29% on steroids, and 21% on omalizumab. Overall, 67% of physicians were satisfied with omalizumab and 33% with sgH1-AH. For patients inadequately controlled on H1-AH, physicians doubled (21%) or quadrupled (32%) H1-AH dose or added omalizumab (11%) or another sgH1-AH (10%). Key treatment goals were improving patients’ QOL (81%) and being free of itch and hives (75%); approximately, half of the physicians (51%) reported success in achieving complete symptom control. Unmet needs included better understanding of CSU etiology (48%), better access to treatments (47%), and reduced administrative barriers for prescribing biologics (45%). Improving patients’ QOL and diagnosis- and treatment-related challenges is critical in CSU management from physicians’ perspective. Despite higher satisfaction with omalizumab, predominant use of sgH1-AH and reluctance to escalate to omalizumab indicate areas for improving treatment strategies in CSU care. Notably, reluctance to escalate to biologics may be partly due to limited availability and barriers to access in certain countries, which must be addressed to optimize care globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".