Hematopoietic Stem Cell Transplantation: a Global Perspective From the Worldwide Network of Blood and Marrow Transplantation.
Notice bibliographique
Résumé
Abstract Abstract 809 Hematopoietic stem cell transplantation (HSCT) has become the standard of care for many patients with defined congenital or acquired disorders of the hematopoietic system. It has seen rapid expansion over the last two decades. HSCT is frequently considered as high cost and highly specialized medicine restricted to countries with abundant resources. This view needs to be changed; HSCT might represent the most cost effective therapy in certain situations. In an attempt to obtain a global overview, the WBMT, has collected information from 1,350 transplant centers in 71 reporting countries over all continents on the numbers of HSCT by indication and donor type for 2006. Data were analyzed by four regions, based on the WHO classification (www.who.org): America (North, Central and South America), Asia (South East Asia and Western Pacific, including Australia and New Zealand), Europe and EMRO/Africa (Eastern Mediterranean region and Africa). Main indications were compared within and between regions. Transplant rates (number of HSCT per 10 million inhabitants) were computed and compared with several macro-economic health care indicators by single and multiple linear regression analyzes. They included gross national income per capita (GNI/capita), total health care expenditures, governmental health care expenditures, adult, infant and maternal mortality rate, hospital beds, cesarean section rates and human developmental index (http://hdr.undp.org). There were a total of 51,421 first HSCT, 22,163 allogeneic (43%), 29,258 autologous (57%). Main indications were leukemias 17,553 (34%; 89% allogeneic), lymphomas 27,778 (54%; 87% autologous), solid tumors 2,954 (6%; 95% autologous) and non-malignant disorders 2,771 (5%; 93% allogeneic). There were significant differences between and within regions: autologous HSCT was the preferred type of HSCT in America (58%) and Europe (61%), allogeneic HSCT in Asia (57%) and in EMRO Africa (65%). The proportion of unrelated donors was highest in Asia (49%); it was negligible in EMRO/Africa (6%). Leukemia was the main indication for allogeneic HSCT globally (71%). Non-malignant/congenital diseases represented about 10% of all HSCT globally; with almost 40% activity reported in EMRO/Africa. A minimum income as measured by GNI per capita and a minimum size as measured by its population or size were the primary prerequisites for performing HSCT in an individual country. No transplants were performed in countries with less than 300 000 inhabitants, less than 960 km2 of size and less than 680 US$ GNI per capita. All macro-economic factors has a significant positive or negative (mortality ratios) association with transplant rates (p<0.05; t-test) but with variable explanatory content: Governmental Health Care Expenditures (r2= 77.33), Gross National Income per Capita (r2= 74.04), team density (r2= 76.28) and, Human Developmental Index (r2= 74.36) explained best transplant rates. Weak explanations were found with, adult (r2= 49.03), infant (r2= 66.31) and maternal mortality rate (r2= 63.21), hospital beds (r2= 32.04) or, caesarean section rates (r2= 30.56). If all factors are combined in regression analyzes explanatory content reaches r2 = 84.24 but the significance of human development index is lost due to multicolinearity. In conclusion, this first global overview on HSCT activity demonstrates that HSCT is an accepted therapy world-wide today, with different needs and priorities in different countries. Transplant activity is concentrated in countries with higher health care expenditures, highest GNI/capita and high team density; hence, governmental support, access to a transplant center, disease prevalence and availability of resources are the key factors related to regional transplant activity. These data provide a solid basis for up-to-date health care counseling and targeted interventions and support the establishment of comprehensive regional registries. Disclosures: Gratwohl: AMGEN: Research Funding; Bristo Myers Squibb: Research Funding; Roche: Research Funding; Novartis: Research Funding; Pfizer: Research Funding.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».