MétaCan
Menu
Back to cohort
Record W2586430923 · doi:10.1182/blood.v112.11.346.346

Non-Myeloablative Hematopoietic Stem Cell Transplantation in Older Patients with AML and MDS: Results from the Center for International Blood and Marrow Transplant Research (CIBMTR)

2008· article· en· W2586430923 on OpenAlexaff
Brian McClune, Daniel J. Weisdorf, John F. DiPersio, Armand Keating, Tanya L. Pedersen, Gisela Tunes da Silva, Jorge Sierra, Martin S. Tallman, Sergio Giralt

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineFludarabineInternal medicineTransplantationUnivariate analysisHematopoietic stem cell transplantationIncidence (geometry)Graft-versus-host diseaseSurgeryOncologyMultivariate analysisChemotherapyCyclophosphamide

Abstract

fetched live from OpenAlex

Abstract Background: AML and MDS disproportionately affect older-aged individuals. Hematopoietic cell transplantation (HCT) is the best established curative therapy but is generally not offered due to concerns about toxicity and poor outcome. Reduced-intensity conditioning (RIC) regimens have been developed to allow allografting in older patients; however, there is a paucity of data to support transplantation in patients over 65 years of age. Purpose: To better study age as a predictor of outcome, we retrospectively analyzed data reported to the CIBMTR from 1995–2005 among patients receiving RIC HCT for MDS (551 patients) and AML (565 patients) in first complete remission (CR). Patient and Methods: Outcomes analyzed for both disease groups included transplant-related mortality (TRM), engraftment, incidence of acute and chronic graft-versus-host disease (GVHD), leukemia-free (LFS) and overall survival (OS). Patients were stratified according to age cohorts for comparison: 40–54, 54–59, 60–64 and ≥65 years. Results: Clinical characteristics were well matched across age cohorts but notably, most AML patients presented with de novo disease (P=0.001) and received their allograft from a matched related donor (MRD) (P=0.001) with 51% of patients ≥65 years having a MRD. MDS patients more often had unrelated donors (URD), especially in the older cohorts (73% for ≥ 65 years); but donor type was not significantly different between groups. Most patients received peripheral blood (PB) allografts (76–97%), fludarabine-containing regimens for conditioning and cyclosporine-containing regimens for GVHD prophylaxis. Univariate analysis demonstrated no statistically significant differences in TRM across age cohorts and no overall difference in occurrence of acute (31–35% at 100 days) or chronic GVHD (36–53% at 2 years). Relapse rates were similar across all age groups (29–39% at 3 years) (Table). Multivariate analysis revealed no statistically significant impact of age on TRM, relapse, LFS, or OS (all p > 0.4). Disease and status at transplant were significant risk factors for OS/LFS at 1 year while affecting TRM/relapse at 2 years. Performance status and HLA disparity were also significant at 2 years for nearly all outcomes. Conclusion: 1. The outcomes for older adults undergoing allogeneic HCT are not significantly different than for younger adults, even after adjusting for multiple risk factors; 2. Age by itself should not be the limiting factor for proceeding to allogeneic HCT in older patients with AML or MDS; 3. Continued participation in clinical trials should be encouraged to explore strategies that could improve treatment outcome. Univariate probabilities of patients age ≥40 years receiving allogeneic HCT for AML/MDS in first complete remission reported to the CIBMTR, 1995–2005. N 40–54 N 55–60 N 60–64 N >65 AML TRM 220 150 132 63 100 days 11 (7–16)% 6 (3–10)% 13 (8–20)% 10 (4–18)% 1 year 20 (15–26)% 18 (12–24)% 24 (17–33)% 30 (19–42)% Relapse 1 year 27 (21–33)% 34 (26–42)% 31 (23–40)% 22 (12–33)% 3 years 32 (26–39)% 35 (27–43)% 39 (30–49)% 33 (21–46)% LFS 1 year 53 (46–60)% 49 (41–58)% 44 (35–53)% 48 (36–61)% 3 years 43 (36–51)% 41 (32–50)% 27 (19–37)% 34 (22–47)% OS 100 days 84 (78–88)% 92 (87–96)% 83 (76–89)% 89 (80–95)% 1 year 59 (52–65)% 60 (52–68)% 51 (42–60)% 51 (39–64)% 3 years 45 (40–54)% 47 (42–59)% 30 (25–43)% 36(24–49)% Follow-up (months) 37 (2–110) 25 (1–87) 36 (3–96) 29 (3–59) MDS TRM 219 150 127 55 100 days 17 (13–23)% 17 (11–23)% 14 (9–21)% 19 (9–30)% 1 year 31 (24–37)% 33 (25–41)% 32 (24–41)% 34 (22–47)% Relapse 1 year 26 (20–32)% 27 (20–35)% 26 (18–34)% 25 (14–37)% 3 years 29 (23–35)% 29 (22–37)% 31 (23–40)% higher 33 (20–47)% LFS 1 year 43 (36–50)% 40 (32–49)% 43 (34–51)% 42 (29–56)% 3 years 36 (29–43)% 27 (–2035)% 29 (21–39)% 23 (12–38)% OS 100 days 77 (71–82)% 77 (70–83)% 81 (74–87)% 76 (64–87)% 1 year 50 (43–56)% 46 (38–54)% 53 (44–62)% 48 (35–61)% 3 years 39 (32–46)% 29 (22–37)% 30 (21–40)% 29 (17–43)% Follow-up (months) 36 (2–86) 40 (3–86) 35 (3–68) 36 (3–85)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.270
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicAcute Myeloid Leukemia ResearchFrench-language works237,207