Cost-Effectiveness Analysis of Efgartigimod vs Chronic Immunoglobulin for the Treatment of Myasthenia Gravis in Canada
Bibliographic record
Abstract
INTRODUCTION: Generalized myasthenia gravis (gMG) is a chronic neuromuscular disease that causes muscle weakness and fatigue, severely impairing quality of life. Efgartigimod is a novel drug that is recently approved for treatment of acetylcholine receptor antibody-positive (AChR-Ab+) gMG patients in Canada. In clinical practice, it is expected to be used in AChR-Ab+ gMG patients who continue to experience symptoms despite conventional therapy and primarily replace chronic immunoglobulins. METHODS: A Markov model was developed to estimate costs and benefits (measured as quality-adjusted life years [QALYs]) of efgartigimod and chronic immunoglobulins for AChR-Ab+ gMG patients. The analysis was conducted from the perspective of the Canadian publicly funded healthcare system over a lifetime horizon. The model comprised six health states based on Myasthenia Gravis Activities of Daily Living (MG-ADL) scores: MG-ADL < 5, MG-ADL 5-7, MG-ADL 8-9, MG-ADL ≥ 10, myasthenic crisis or death. Health state transition probabilities were estimated from the ADAPT and ADAPT+ studies, plus a network meta-analysis that compared efgartigimod against chronic immunoglobulins. The MyRealWorld MG study informed utility values. Modeled costs included treatment and administration, disease monitoring, complications from chronic corticosteroid use, exacerbation/crisis management, adverse events and end-of-life care. RESULTS: Over a lifetime horizon, efgartigimod and chronic immunoglobulins were predicted to have total discounted QALYs of 16.80 and 13.35 and total discounted costs of $1,913,294 and $2,170,315, respectively. Efgartigimod dominated chronic immunoglobulins with incremental QALYs of 3.45 and cost savings of $257,020. CONCLUSIONS: Efgartigimod provides greater benefit in terms of lower costs than chronic immunoglobulins for AChR-Ab+ gMG patients in Canada.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".