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
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".