Cost-effectiveness of idecabtagene vicleucel compared with conventional care in triple-class exposed relapsed/refractory multiple myeloma patients in Canada and France
Bibliographic record
Abstract
The chimeric antigen receptor (CAR) T-cell therapy idecabtagene vicleucel (ide-cel) is approved for the treatment of adult patients with relapsed/refractory multiple myeloma (RRMM) who have already received an immunomodulatory agent, a proteasome inhibitor, and an anti-CD38 antibody and have progressed on their last therapy. The objective of this study was to assess the cost-effectiveness of ide-cel versus conventional care in Canada and France. A partitioned survival model was used to estimate the cost-effectiveness of ide-cel (target dose 450 × 106 CAR T cells) in its approved indication in terms of life-years (LYs), quality-adjusted LYs (QALYs), and costs. Patient-level data from the KarMMa Phase II clinical trial (clinicaltrials.gov NCT03361748) and KarMMa-RW study were used to inform the model; overall and progression-free survival were extrapolated using standard parametric functions after the observed periods. The model adopted Canadian and French societal perspectives over a lifetime horizon. Costs, utilities, discounting (Canada: 1.5%, France: 2.5%), and general population mortality were country-specific. The base case demonstrated that ide-cel was associated with more additional LYs (+2.64 and +2.51) and QALYs (+2.31 and +2.54) than conventional care at incremental costs of CAN$588,490 and €392,251 in Canada and France, respectively. The resulting incremental cost-effectiveness ratio (ICER) for ide-cel was $255,245 per QALY in Canada, and €154,593 per QALY in France. Ide-cel was associated with significant survival improvements in terms of both LYs and QALYs in patients with progressive triple-class-exposed RRMM. The ICER for ide-cel was similar to that of other approved and reimbursed RRMM therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".