A Comparison of the Efficacy of Immunomodulatory-containing Regimens in Relapsed/Refractory Multiple Myeloma: A Network Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Previous network meta-analyses combined studies of immunomodulatory drug (IMiD)-containing and IMiD-free regimens, despite a lack of head-to-head randomized controlled trials to robustly link them. However, patients with relapsed or refractory multiple myeloma (RRMM) treated with IMiD-containing regimens differ from those treated with IMiD-free regimens, especially relating to treatment history, which is an important treatment-effect modifier requiring clinical consideration when evaluating the most appropriate subsequent treatment options. A need exists to separately assess the efficacy of treatment regimens for patients who are suitable candidates for IMiD-containing and IMiD-free regimens. The presented analyses will enable clinicians to assess the best regimens to use in patients suitable for IMiD-containing regimens. MATERIALS AND METHODS: We used a Bayesian network meta-analysis to compare IMiD-containing regimens in patients with RRMM. Additionally, subgroup analyses were conducted stratified by previous therapy line, previous bortezomib therapy, and previous lenalidomide therapy. RESULTS: The results indicated that triplet combinations are more effective than doublet combinations. Of the triplet combinations, daratumumab, lenalidomide, dexamethasone (DRd) was significantly better in improving progression-free survival in patients with RRMM than were other IMiD-containing regimens (lenalidomide, dexamethasone [Rd]: hazard ratio [HR], 0.37; carfilzomib, Rd: HR, 0.54; elotuzumab, Rd: HR, 0.54; ixazomib, Rd: HR, 0.50). Similar trends were observed for overall survival and overall response. DRd showed the greatest probability of being the best treatment for all clinical efficacy outcomes. The subgroup analyses results were consistent with the base-case results. CONCLUSION: In patients with RRMM who are suitable for an IMiD-containing regimen, DRd showed clear advantages in survival and response outcomes compared with other IMiD-containing regimens.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".