Real-World Anti-Tumor Necrosis Factor Treatment in Rheumatoid Arthritis, Psoriatic Arthritis, and Ankylosing Spondylitis: Cost-Effectiveness Based on Number Needed to Treat to Improve Health Assessment Questionnaire
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness and cost-effectiveness of anti-tumor necrosis factor (anti-TNF) medications in a real-world environment for the treatment of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) using the Health Assessment Questionnaire (HAQ). METHODS: We created a database of patients with RA, PsA, or AS treated with anti-TNF agents (etanercept, infliximab, or adalimumab) at a large outpatient rheumatology clinic. Patient characteristics, baseline HAQ prior to treatment, subsequent yearly HAQ, and reasons for termination were collected. The cost based on percentage of patients achieving >or= 0.2 improvement in HAQ (minimal clinically important difference, MCID) was calculated using the 2008 direct cost (Cdn) of the medication. RESULTS: Data were available on 297 patients (206 with RA, 57 PsA, 34 AS). The mean age was 55 years, with 12 years of disease, and the mean baseline HAQ (standard error, SE) was 1.37 (0.04). The changes in HAQ (SE) at Years 1, 2, and 3 were -0.31 (0.04), -0.24 (0.06), and -0.27 (0.07) for annual cost to achieve MCID of $41,636, $42,077, and $42,147, respectively. The number needed to treat (NNT) was 1.94 (RA), 1.88 (PsA), and 2.30 (AS). There were no statistical differences between the diseases studied. CONCLUSION: We obtained data on the effectiveness and cost-effectiveness of anti-TNF drugs using the HAQ score, which is known to be an excellent predictor of work disability, morbidity, and mortality. HAQ scores decreased with treatment and were sustained throughout the 3-5 years of followup. The NNT of approximately 2 seems favorable and was similar between diseases.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".