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Record W4412775412 · doi:10.31547/bct-2025-001

Racial Disparity in Myeloablative Hematopoietic Cell Transplantation Outcomes in Patients with Hematological Malignancies Older Than 45 Years

2025· article· en· W4412775412 on OpenAlexaff
Satarupa Mohapatra, Yasser R. Abou Mourad, Hannah Cherniawsky, Shanee Chung, Donna L. Forrest, Gagan Kaila, Florian Kuchenbauer, Katie Lacaria, Joanna E. MacLean, Stephen H. Nantel, Sujaatha Narayanan, Thomas J. Nevill, Judith Anula Rodrigo, A. MAUREEN ROUHI, Claudie Roy, David Sanford, Kevin Song, Ryan J. Stubbins, Cynthia L. Toze, Deepesh Lad

Bibliographic record

VenueBLOOD CELL THERAPY / The official journal of APBMT · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of British ColumbiaLeukemia & Lymphoma Society of Canada
Fundersnot available
KeywordsHematopoietic cellMedicineHematopoietic stem cell transplantationHematologic NeoplasmsOncologyHaematopoiesisTransplantationInternal medicineStem cellBiologyGenetics

Abstract

fetched live from OpenAlex

Introduction:The impact of race on outcomes of allogeneic hematopoietic cell transplants (HCT) has long been a field of research.The Center for International Blood and Marrow Transplant Research (CIBMTR) studies have shown worse survival for Black and Hispanic patients within the first year after HCT, but rates evened out for one-year survivors.From our personal experience, we hypothesize that the outcomes of South Asians (age ≥ 45 years) receiving myeloablative conditioning (MAC) are also worse compared to other races.Methods: This is a retrospective single-centre study.All patients (age ≥ 45 years) undergoing MAC-HCT for hematological malignancies from 2011-2022 were included.The primary outcome was overall survival (OS).Secondary outcomes were non-relapse mortality (NRM), incidence of grade 2-4 acute graft versus host disease (GVHD), moderate-severe chronic GVHD and relapse incidence (RI).The survival analysis was performed using Kaplan-Meier analysis and log-rank test.The GVHD, NRM and RI rates were calculated using the cumulative incidence (CI) of competing events and the Gray test.EZR was used for statistical analysis.Results: Of the 483 patients included, there were 28 (5.8%)South Asians (SA), 73 (15.1%), other Asians (East Asians (EA)/Southeast Asians (SEA), and 382 (79.1%)Whites (W).Asians were less likely to get matched unrelated donor-HCT than Whites (SA 21%, EA/SEA 30%, W 45%, p=0.009).The three groups were comparable regarding the recipient and donor sex and performance status.The proportion of SA with HCT-CI ≥ 3 was significantly higher (SA 50%, EA/SEA 37%, W 31%, p=0.03).SA patients were more likely to be obese (body mass index ≥ 30 kg/m 2 ) (SA 29%, EA/SEA 5%, W 19%, p=0.005).There were fewer cytomegalovirus (CMV) serological mismatches among the Asians (SA 25%, EA/SEA 26%, W 43%, p=0.009).There was no difference in the conditioning type and CD34 cell dose.However, fewer Asians received Antithymocyte globulin/post-transplant cyclophosphamide as GVHD prophylaxis (SA 39%, EA/SEA 42%, W 45%, p=0.0009).The median OS was significantly shorter in SA (SA 19, EA/SEA 103, W 65 months, p=0.04).The 2-year NRM was significantly higher in SA (SA 35.7%, EA/SEA 13.7%, W 16%, p=0.03).The CI of grade 2-4 acute and moderate-severe chronic GVHD was not significantly different (p=0.7 & 0.6).The 2-year RI was also not significantly different (SA 28.5%, EA/SEA 24.7%, W 28%, p=0.8).Conclusion: Our study confirms that South Asians aged ≥ 45 years have worse survival after MAC-HCT.Supportive care is unable to overcome the differences in the outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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