Efficacy of Maintenance Therapies By Prognostic Subgroups in Post-Transplant Newly Diagnosed Multiple Myeloma Patients: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Introduction The optimal maintenance regimen by prognostic categories remains unclear in newly diagnosed multiple myeloma (NDMM) patients undergoing autologous stem cell transplant (ASCT). Therefore, we conducted a systematic review and network meta-analysis (NMA) to assess the comparative effectiveness of novel agents by different prognostic groups. Methods A comprehensive systematically structured literature search was conducted to identify phase II/III randomized controlled trials (RCTs) evaluating maintenance therapies in post-transplant NDMM patients and reporting data by subgroups including but not limited to international staging system (ISS), and cytogenetic risk. High cytogenetic risk (HCR) and standard cytogenetic risk (SCR) were defined in accordance with the eligible trials. Main outcomes of interest were overall survival (OS) and progression-free survival (PFS). Precomputed hazard ratios (HR) with 95% CI were pooled using an inverse variance approach. A frequentist network meta-analysis was conducted. P-score (PS) were used to assess relative rankings of the treatments and were interpreted in congruency with pairwise estimates. Higher ranks indicated better efficacy. Results This NMA included six RCTs with 4119 participants and six unique treatment arms. In patients with SCR, the combination of carfilzomib, lenalidomide, and dexamethasone (KRd) improved PFS when compared to lenalidomide (Len; HR: 0.44; 95% CI: 0.24-0.81), daratumumab (Dara; 0.27; 0.13-0.55), ixazomib (Ixa; 0.26; 0.13-0.53), and no maintenance therapy (0.17; 0.09-0.32). Similarly, KR improved PFS compared to lenalidomide (Len; 0.56; 0.33-0.96), daratumumab (Dara; 0.34; 0.18-0.65), ixazomib (Ixa; 0.33; 0.17-0.63), and no maintenance therapy (0.21; 0.12-0.38). There was no statistically significant difference between KRd and KR. KRd was ranked as potentially the most efficacious treatment (rank 1) followed by KR (rank 2), Len (rank 3), Dara (rank 4), and Ixa (rank 5) for improving PFS in SCR. In patients with HCR, KRd (0.35; 0.14-0.92), KR (0.32; 0.15-0.67), Dara (0.43; 0.25-0.73), and Len (0.48; 0.35-0.64) improved PFS when compared to no maintenance therapy. No other significant differences were observed in HCR patients. KR was ranked as potentially the most efficacious treatment followed by KRd (rank 2), Dara (rank 3), Len (rank 5), and Ixa (rank 5) for PFS in HCR patients. In patients with ISS-I/II, KRd and KR improved PFS when compared to Dara (KRd vs. Dara - 0.32; 0.16-0.65, KR vs Dara - 0.50; 0.26-0.94), and Ixa (KRd vs. Ixa - 0.22; 0.11-0.45, KR vs Ixa - 0.35; 0.18-0.65). KRd, not KR, improved PFS when compared to Len (0.41; 0.22-0.76). Rankings were consistent with those observed in SCR patients. No significant differences were observed among mixed treatment comparisons for ISS-III patients. Limited number of trials reporting OS precluded formal assessment. Conclusions The current data suggest that KRd and KR may delay disease progression regardless of risk category. Overall survival data is still emerging and, if consistent, may influence clinical practice. We maintain a living meta-analysis to incorporate new evidence constantly.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".