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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".