Matching-adjusted indirect comparison of efficacy outcomes for ciltacabtagene autoleucel in CARTITUDE-1 versus idecabtagene vicleucel in KarMMa for the treatment of patients with relapsed or refractory multiple myeloma
Bibliographic record
Abstract
Objective This study estimated the comparative efficacy of ciltacabtagene autoleucel (cilta-cel) versus the approved idecabtagene vicleucel (ide-cel) dose range of 300–460 × 106 CAR-positive T-cells for the treatment of patients with relapsed or refractory multiple myeloma (RRMM) who were previously treated with a proteasome inhibitor, an immunomodulatory drug, and an anti-CD38 monoclonal antibody (i.e. triple-class exposed) using matching-adjusted indirect treatment comparisons (MAICs).Methods MAICs were performed with individual patient data for cilta-cel (CARTITUDE-1; NCT03548207) and published summary-level data for ide-cel (KarMMa; NCT03361748). Treated patients from CARTITUDE-1 who satisfied the eligibility criteria for KarMMa were included in the analyses. The MAIC adjusted for unbalanced baseline covariates of prognostic significance identified in the literature and by clinical expertise. Comparative efficacy was estimated for overall response rate (ORR), complete response or better (≥CR) rate, duration of response (DoR), progression-free survival (PFS), and overall survival (OS).Results Cilta-cel was associated with statistically significantly improved ORR (odds ratio [OR]: 94.93 [95% confidence interval [CI]: 21.86, 412.25; p < .0001]; relative risk [RR]: 1.34), ≥CR rate (OR: 5.49 [95% CI: 2.47, 12.21; p < .0001]; RR: 2.21), DoR (hazard ratio [HR]: 0.50 [95% CI: 0.29, 0.87; p = .0137]), and PFS (HR: 0.37 [95% CI: 0.22, 0.62; p = .0002]) when compared with ide-cel. For OS, the results were in favor of cilta-cel and clinically meaningful but with a CI overlapping one (HR: 0.55 [95% CI: 0.29, 1.05; p = .0702]).Conclusions These analyses demonstrate improved efficacy with cilta-cel versus ide-cel for all outcomes, highlighting its therapeutic potential in patients with triple-class exposed RRMM.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".