Economic evaluation of systemic therapies for moderate to severe psoriasis
Bibliographic record
Abstract
BACKGROUND: New biologics have dramatically changed therapeutic options for psoriasis, albeit at additional cost. OBJECTIVES: To determine the cost-effectiveness and optimal treatment sequence for moderate to severe psoriasis. METHODS: Psoriasis Area and Severity Index (PASI) response rates from 22 randomized controlled trials evaluating biologic (adalimumab, efalizumab, etanercept, infliximab) and nonbiologic systemic (methotrexate, ciclosporin) agents were considered. Short-term efficacy was based on relative probabilities of achieving PASI response (50/75/90) in a meta-analysis of trials. Published evidence and assumptions were used to predict long-term efficacy. Treatment benefits were determined by the relationship between PASI response and the EuroQOL 5D health utility measure. Costs included therapy, administration, monitoring and hospitalization. Incremental cost-effectiveness ratios (ICERs) were calculated and treatments ranked relative to supportive care. RESULTS: Infliximab provided the most incremental quality-adjusted life-years (QALYs) vs. supportive care (0.18 QALYs; 95% confidence interval, CI 0.13-0.24), followed by adalimumab (0.16 QALYs; 95% CI 0.11-0.22). Methotrexate and ciclosporin were less beneficial (0.13 and 0.08 QALYs, respectively) but were cost saving and considered the first two treatments in the optimal sequence. Comparing biologics, adalimumab was most cost effective (ICER pound30 000 per QALY), followed by etanercept ( pound37 000 per QALY), efalizumab ( pound40 000 per QALY) and infliximab ( pound42 000 per QALY). CONCLUSIONS: Methotrexate and ciclosporin are cost effective but require monitoring for toxicities. Of the biologics, adalimumab was most cost effective following conventional systemic treatment failure or inadequate response. Payers and policymakers will have to decide how to utilize their budgets effectively for treating patients with moderate to severe psoriasis.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".