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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,016 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».