Survival Following Quadruplet and Triple Therapy in Transplant in-Eligible Multiple Myeloma - a Meta-Analysis of Recent Clinical Trials
Bibliographic record
Abstract
INTRODUCTION In the management of transplant-ineligible multiple myeloma (TIMM), therapeutic strategies are crucial for improving patient outcomes. Recent clinical trials have explored the efficacy of Quadruplet therapy and Triplet therapy in this context. Quadruplet therapy, involving an additional drug, has been proposed to offer enhanced benefits in overall survival and progression-free survival. AIM This meta-analysis aimed to evaluate the comparative effectiveness (overall survival [OS] and progression-free survival [PFS]) of Quadruplet vs Triplet therapy in TIMM regimens to inform clinical decision-making and optimize patient care. METHODS Type of Study: Meta-analysis of Phase 3 clinical trials Endpoint: Evaluate OS and PFS in Quadruplet vs Triplet therapy Search strategy: PRISMA protocol using keywords ((“multiple myeloma”[Title/Abstract] AND (“quadruple therapy”[Title/Abstract] OR “triple therapy”[Title/Abstract] OR (“bortezomib-lenalidomide-dexamethasone”[All Fields] AND “VRd”[Title/Abstract]) OR (“lenalidomide-dexamethasone”[All Fields] AND “Rd”[Title/Abstract]) OR (“bortezomib-melphalan-prednisone”[All Fields] AND “VMP”[Title/Abstract]) OR (“CD38-targeted”[All Fields] AND “antibody”[Title/Abstract]))) NOT “Transplant-eligible”[All Fields]) AND ((y_10[Filter]) AND (clinicaltrial[Filter] OR clinicaltrialphasei[Filter] OR clinicaltrialphaseii[Filter] OR clinicaltrialphaseiii[Filter] OR clinicaltrialphaseiv[Filter] OR multicenterstudy[Filter] OR observationalstudy[Filter] OR randomizedcontrolledtrial[Filter]) AND (fft[Filter]) AND (humans[Filter]) AND (english[Filter])) to identify clinical trials from last 10 years. Inclusion criteria: Only Clinical trials recruited TIMM patients in the last 10 yearsExclusion criteria: Studies other than clinical trials, non-human, non-English, and non-full text studies were excluded. Study selection & Data extraction: Using eligibility criteria and keywords, we screened the abstracts and evaluated them for their inclusion in our meta-analysis. From screen abstracts, full-length articles were obtained and studied individually for their eligibility in quantitative analysis (meta-analysis). Data on study name, design, country, duration, sample size, population characteristics [country, mean/median age, sex], type of intervention, and outcomes (OS and PFS) were collected. Statistical analysis: Review Manager 5.3 software was used to analyze the data. We performed random effects models to estimate the pooled effect size (pooled odds ratio) and 95% confidence interval (95% CI). Forest plots were obtained. p<0.05 was considered statistically significant. Heterogeneity (I2 values) was identified and I2>75% represented high heterogeneity. Risk of bias analysis was performed using the Newcastle-Ottawa Scale. RESULTS Out of 51 studies, fulfilling the criteria, nine studies had data on the management of TIMM, of which five studies had data on outcomes of Quadruplet vs Triplet therapy. ALCYONE, IMROZ, and OCTANS were the main multicenter, randomized, and open-label clinical trials covering 160+ sites in 25+ countries. We found 607 patients (of 1196) on Quadruplet and 368 patients (of 1142) on Triplet therapy. Quadruplet therapy had 88% higher odds of OS [OR: 1.88, 95%CI: 1.51-2.35, p<0.00001, I2: 0%] and 178% higher odds of PFS [2.78, 0.83-9.33, p=0.1, I2: 92%] in compared to Triplet therapy. [Pooled OR for survival: 2.30, 1.36-3.87, p=0.002, I2: 0%]. Quadruplet therapy had higher odds of MRD negative [4.04, 2.32-7.03, p<0.00001, I2: 52%] and RR [2.25, 1.66-3.05, p<0.00001, I2: 0%] in compared to Triplet therapy. NOS suggested a moderate risk of bias. CONCLUSION In conclusion, our meta-analysis demonstrates that Quadruplet therapy significantly outperforms Triplet therapy in transplant-ineligible multiple myeloma patients, with notable improvements in overall survival and progression-free survival. The Quadruplet regimen also enhances the likelihood of achieving minimal residual disease negativity and a higher response rate. These findings underscore the potential benefits of integrating an additional drug into treatment regimens for better clinical outcomes.
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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.061 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".