Global Use and Trends in Hematopoietic Stem Cell Transplantation Analyzed by the Worldwide Network of Blood and Marrow Transplantation WBMT: A Targeted Approach for a Widening Gap
Notice bibliographique
Résumé
Abstract Abstract 1016 Transplantation of cells, tissues and organs is recognized by the World Health Organization (WHO) as a global task, no longer restricted to affluent countries. Still, there are few data relating to its use and trends on a global level and the macroeconomic factors associated with it. Data from 146,808 patients (pts) with hematopoietic stem cell transplantations (HSCT), 66,226 allogeneic (allo 45%), 80,582 autologous (auto 55%) from 1407 centers in 70 countries were used to describe the current status and to analyze trends over the period from 2006 to 2008. Transplant rates (TR, number of HSCT/10 million inhabitants) and their changes from 2006 to 2008 were assessed by main indication and donor type (leukemias (52,322 pat (36%), 47,674 allo, 4,648 auto); lymphoproliferative disorders (77,237 pts (53%), 9,846 allo, 67,391 auto); solid tumors (8,057 pat (5%), 399 allo, 7,658 auto) and non-malignant disorders and others (9,192 pts (6%), 8,307 allo, 885 auto) for each participating country and its corresponding WHO region America (42,470 pts (29%), 19,463 allo, 23,007 auto), Asia (including South-East Asia and Western Pacific) (25,931 pts (18%), 15,547 allo, 10,384 auto), Eastern-Mediterranean/Africa (3,986 pat (3%), 2,509 allo, 1,477 auto) and Europe (74,421 pat (51%), 28,707 allo, 45,714 auto). The associations of TR with Gross National Income per Capita (GNI/cap) and, for unrelated donor HSCT, with presence or absence of an unrelated donor registry were calculated by linear regression analyses. Proportions of donor type (p<0.01) and main indications (p<0.01) differed significantly between regions. TR ranged from 0 to 781 (median 124) for total, from 0 to 454 (median 49) for allo and from 0 to 536 (median 74) for auto HSCT. TR showed a significant association with lnGNI/cap (R2=58.6 for total HSCT), independent of WHO region. This association differed substantially by donor type and main indication, with a greater impact and a higher explanatory content of lnGNI/cap on TR for auto (R2=54.9) than allo HSCT (R2=48.7). Explanatory content was highest in auto HSCT for plasma cell disorders (R2=53.2); it was greater for acute (R2=48.7) than for chronic leukemias (R2=31.2) and was nearly absent for allo family donor HSCT in non malignant disorders (R2=4.4). This lack of association between non malignant disorders as indication for allo HSCT and GNI/cap is additionally illustrated by a higher proportion of patients with this indication for allo HSCT in countries with lower GNI/cap (10% in low, 7% in middle and 5% in high income countries). Variation in unrelated donor TR was explained by lnGNI/cap (R2=46.7), presence of a national donor registry (R2=29.8) and number of locally registered donors (R2=14.7) as single explanatory factors, and by all three to an extent of R2=59.3 in a multiple regression. Numbers of HSCT increased from 40,524 in 2006 to 43,576 in 2008. The high income countries exhibited a positive trend (p=0.02; total HSCT), not so the middle (p=0.57) and low income (p=0.35) countries. This trend was most marked for unrelated donor HSCT for acute leukemias (p=0.004) in high income countries. Increase in TR was positively but not significantly (p=0.13; total HSCT) associated with lnGNI/cap, suggesting a widening gap between more or less affluent countries. These data form the basis for a targeted approach to optimize HSCT on a global level. Unrelated donor registries are recommended for all countries performing HSCT. Transplant organizations should concentrate on refining indications for patients with acute leukemias (allo) and lymphoproliferative disorders (auto) in high income countries, for patients without need for intensive pretreatment such as chronic leukemias and non malignant disorders (allo) in lower income countries. Disclosures: No relevant conflicts of interest to declare.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».