The Global State of Hematopoietic Stem Cell Transplantation for Multiple Myeloma: An Analysis of the Worldwide Network of Blood and Marrow Transplantation (WBMT) Database and the Global Burden of Disease Study
Bibliographic record
Abstract
Background: Multiple myeloma (MM), is a clonal plasma cell neoplasm characterized by destructive bony lesions, anemia, and renal impairment. MM is a global disease - worldwide in 2016, there were 138509 incident cases with an age standardized incidence rate (ASIR) of 2.1 per 100 000 persons, with a 126% global increase in incident cases from 1990 to 2016 (Cowan AJ et al JAMA Oncology 2018). Access to effective care, including proteasome inhibitors, immunomodulatory agents, and autologous hematopoietic stem cell transplantation (HSCT) is largely limited to high-income sociodemographic index (SDI) countries. SCT remains the standard of care for eligible patients, and in general is more affordable and accessible worldwide than novel therapies. We sought to evaluate the rates and utilization of ASCT globally from 2006-2015 to better characterize access to SCT for patients with MM. Methods: This was a new analysis of a retrospective survey of WBMT sites, conducted annually between 2006-2015, as described previously (Niederwieser et al BMT 2016). Incidence data estimates were reported from the Global Burden of Disease study (Institute for Health Metrics and Evaluation. 2019 'GBD Results Tool.' Global Health Data Exchange. Seattle WA: University of Washington. Accessed 1 June 2019). South Asia and East Asia regions were combined for this analysis. Outcome measures included total number of autologous and allogeneic stem cell transplants by World Bank (WB) regions, and percentage of newly diagnosed MM patients who underwent ASCT, calculated by the number of transplants per region in calendar year / gross annual incidence of MM per region. Results: From 2006 to 2015, the number of autologous HSCT performed worldwide for MM increased by 107% (Figure 1). Activity increased in each region from 2006 to 2015 from 56% in USA and Canada to 335% in Latin America. Utilization of autologous HSCT was highest amongst the Northern America and European WB regions, with an increase from 13% to 24% in Northern America, and an increase from 15% to 22% in Europe. The activity increased considerably in the Latin American countries (335,46% increase) and the utilization reached >10%. In contrast, the utilization of autologous HSCT was much lower in the Africa/Mediterranean and Asian/Pacific region, with autologous HSCT utilization only changing marginally from 1.8% in 2006 to 4% in 2015 despite increase in activity. The number of first allogeneic HSCT performed globally for MM declined after a peak in 2012 by -3% since 2006 mostly in North America. Allogeneic HSCT remains highest amongst the European WB region (increase 8%). The increase in activity was accompanied by an increase in team numbers from 1327 in 2006 to 1581 in 2015 but also by an increase of activity in the teams. Discussion: Autologous HSCT utilization has increased worldwide in high-income SDI WB region countries for MM yet has not increased proportionally amongst low-middle income WB regions. There is a disparity in autologous HSCT utilization amongst high-income regions, exceeding 20% in North America and Europe, while remaining poorly utilized in Africa and the East Mediterranean. Latin America has increased their utilization and is for the first time above 10%. However, we are limited with respect to use of incidence data in LMIC countries from the GBD, likely due to under reporting. Conflicting clinical trial data likely contributed to the decline in some regions for first allogeneic HSCT in MM. More work is needed to improve access to transplantation services for MM patients, especially in low to middle income countries. Conclusion: Although autologous HSCT numbers and rates have increased globally, there are marked disparities in usage amongst high versus low to middle income countries. More work is needed to improve access to HSCT for MM globally. Figure 1 Disclosures Cowan: Celgene: Consultancy, Research Funding; Cellectar: Consultancy; Juno: Research Funding; Sanofi: Consultancy; Abbvie: Research Funding; Janssen: Consultancy, Research Funding. Atsuta:Janssen Paharmaceutical K.K.: Honoraria; Kyowa Kirin Co., Ltd: Honoraria; Chugai Pharmaceutical Co., Ltd.: Honoraria; Mochida Pharmaceutical Co. Ltd: Honoraria. Worel:Sanofi Genzyme, Malinckrodt Therakos: Research Funding; Jazz, Sanofi, Celgene, Novartis, Malinckrodt Therakos: Honoraria; Sanofi Genzyme, Malinckrodt Therakos: Speakers Bureau. Libby:Alnylam: Consultancy; Abbvie: Consultancy; Pharmacyclics and Janssen: Consultancy; Akcea: Consultancy. Pasquini:Novartis: Research Funding; Kite Pharmaceuticals: Research Funding; BMS: Research Funding; Medigene: Consultancy; Amgen: Consultancy; Pfizer: Consultancy. Galeano:Szabo SA: Other: (Equity interest). Szer:Amgen: Honoraria, Other: Travel, Research Funding; Alexion: Honoraria, Other: Travel, Research Funding; Pfizer: Honoraria, Other: Travel, Research Funding; Sanofi: Honoraria, Other: Travel, Research Funding; Takeda: Honoraria, Other: Travel, Research Funding; Prevail Therapeutics: Honoraria, Other: Travel, Research Funding; Novartis: Honoraria, Other: Travel, Research Funding; MSD: Honoraria, Other: Travel, Research Funding; Celgene: Honoraria, Other: Travel, Research Funding. Kroeger:Neovii: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Riemser: Research Funding; JAZZ: Honoraria; Sanofi-Aventis: Honoraria; Novartis: Honoraria, Research Funding; Medac: Honoraria; DKMS: Research Funding. Weisdorf:Fate Therapeutics: Consultancy; Incyte: Research Funding; Pharmacyclics: Consultancy. Niederwieser:Cellectis: Consultancy; Daichii: Speakers Bureau.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".