Utilization of Multiple Myeloma Treatments in Developing Countries: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: The outcomes of patients diagnosed with multiple myeloma (MM) have improved significantly with the advent of immunomodulatory drugs, proteasome inhibitors, anti-CD38 monoclonal antibodies, and high-dose chemotherapy followed by autologous hematopoietic stem cell transplantation (HSCT). Further advancements include immune-based approaches such as bispecific antibodies and chimeric antigen T-cell receptor therapy. However, most of these therapies are predominantly available in developed countries, with limited access globally. Our study aimed to ascertain the landscape of interventions being used in developing countries for MM, and their efficacy. Methods: A comprehensive search was conducted on March 13, 2023, using Embase via Elsevier, PubMed, Scopus, CINAHL, and CENTRAL. We included retrospective and prospective studies that were evaluating interventions for patients with plasma cell dycrasias in the developing country setting. We defined developing country according to the United Nations. For comparative studies, we calculated the risk ratio (RR) for binary outcomes such as overall survival (OS) and progression-free survival (PFS), estimating 95% confidence intervals (CIs) and pooling data using the DerSimonian and Laird random effects model. For non-comparative studies, we calculated overall event rates and transformed them using the Freeman-Tukey double arcsine method, then pooled the data using the same random effects model. All statistical analyses were conducted using R version 4.4.0. Results: A total of 448 records were identified, of which 37 studies met the eligibility criteria. The overall risk of bias was moderate. The included studies evaluated induction treatments using doublet and triplet-based therapies, including TD (thalidomide and dexamethasone), VD (bortezomib and dexamethasone), MP (melphalan and prednisone), CTD (cyclophosphamide, thalidomide, and dexamethasone), VTD (bortezomib, thalidomide, and dexamethasone), and VAD (vincristine, doxorubicin, and dexamethasone). Lenalidomide was notably absent in the induction regimens apart from one non-comparative study reported on the use of RVD (lenalidomide, bortezomib, and dexamethasone). Studies were divided into comparative (n=11) and non-comparative groups (n=26). Comparative studies included: HSCT vs. no HSCT, induction with alkylating agents vs. thalidomide or bortezomib, and thalidomide vs. bortezomib or CTD vs. VTD. Lenalidomide use in comparative studies was reported only post-HSCT, compared against observation. High-dose chemotherapy followed by autologous HSCT was commonly utilized, with most studies reporting outcomes for patients who underwent autologous HSCT. Two comparative studies from 2 countries (Colombia and India) compared the overall survival of autologous HSCT in patients diagnosed with MM (N=130) vs. patients who never received ASCT (N=293). At one year of follow-up, there was no significant difference in overall survival (OS) between patients who received autologous HSCT and those who did not (relative risk [RR] = 1.11, 95% CI: 0.93-1.33). However, a significant improvement in OS was observed at five years post-autologous HSCT (RR = 1.59, 95% CI: 1.38-1.82). The pooled estimate for five-year OS in single-arm studies, which included nine cohorts from seven countries (Pakistan, India, Colombia, Algeria, Sri Lanka, Mexico, and Peru), was 62% (95% CI: 48-75). Progression-free survival (PFS) at five years post-HSCT, reported in three cohorts from three countries (India, Algeria, and Mexico), was 44% (95% CI: 23-67). Conclusions: Most of the recent advancements in MM treatment, including immunomodulatory drugs, proteasome inhibitors, and anti-CD38 monoclonal antibodies, were not widely utilized globally, particularly in developing countries. This limited access is a major issue, hindering the potential improvement in patient outcomes. However, high-dose chemotherapy followed by autologous HSCT showed good outcomes, demonstrating a significant survival advantage at five years follow-up. The lack of survival benefits at one-year post-HSCT may be due to high toxicity and/or limited resources to manage side effects in developing countries. Addressing the disparity in treatment availability is crucial for improving MM management and patient survival globally.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".