Brodalumab for Plaque Psoriasis: A Canadian Real-World Experience at 2-Years Post-Launch
Bibliographic record
Abstract
BACKGROUND: There is limited real-life evidence with brodalumab in patients with plaque psoriasis in Canada. OBJECTIVES: To examine real-world effectiveness of brodalumab in Canadian routine care with a focus on clinician and patient-reported outcomes, as well as measuring continuation rates and persistency. METHODS: Retrospective analysis was conducted on data collected through the brodalumab patient support program (PSP) in Canada for patients initiating brodalumab between June 2018 (PSP launch)- June 2020 with a minimum of 16 weeks follow-up from first dose. Effectiveness was assessed by improvements in PASI, BSA and DLQI; continuation rates and persistency on therapy were reported. RESULTS: Overall, 864 patients (male, 59%; median age, 52 years) were included in the analysis. In a subset of patients with both baseline and follow-up scores, statistically significant improvements were observed: PASI improved from 13.9 to 1.8, BSA improved from 16.6% to 2.5% and DLQI improved from 16.2 to 2.9. Brodalumab demonstrated high continuation rates (89.9%), with similar rates in biologic-naïve and biologic-experienced patients (92.1% and 88.6%, respectively) and in patients who received secukinumab or ixekizumab as their most recent biologic therapy (89.0% and 86.2%, respectively). Persistence at 6, 12, and 18 months was 82.0%, 69.9%, and 63.4%, respectively. CONCLUSIONS: The effectiveness of brodalumab was demonstrated in this Canadian routine care study, with significant improvements in disease severity and patient-reported outcomes. High continuation rates were achieved; including in patients previously treated with IL-17A inhibitors. Future studies will provide further evidence of brodalumab's benefits for the management of plaque psoriasis in the real-world setting.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".