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Record W4407977051 · doi:10.1016/j.jtct.2025.01.081

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

2025· article· en· W4407977051 on OpenAlexaff
Satarupa Mohapatra, Yasser R. Abou Mourad, Hannah Cherniawsky, Shanee Chung, Donna L. Forrest, Gagan Kaila, Florian Kuchenbauer, Stephen H. Nantel, Sujaatha Narayanan, Thomas J. Nevill, Judith Anula Rodrigo, Claudie Roy, David Sanford, Kevin Song, Ryan J. Stubbins, Cynthia L. Toze, Jennifer White, Deepesh Lad

Bibliographic record

VenueTransplantation and Cellular Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsLeukemia & Lymphoma Society of Canada
Fundersnot available
KeywordsMedicineHematopoietic cellHematopoietic stem cell transplantationHematologic NeoplasmsHaematopoiesisOncologyTransplantationInternal medicineLeukemiaPediatricsStem cellGeneticsBiology

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 Blacks and Hispanics within the first year after HCT but evened out for one-year survivors. These studies included all adult patients aged >18 years. We hypothesize that the outcomes of South Asians (age ≥ 45 years) receiving myeloablative conditioning (MAC) are worse compared to other races. Objective To compare the outcomes after MAC-HCT for hematological malignancies among different racial groups (age ≥ 45 years) Methods This was a single-center retrospective study conducted at the Leukemia/ BMT Program of BC. 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 done 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 504 patients, there were 28 (5.5%) South Asians (SA), 73 (14.5%) Other Asians (East Asians (EA)/Southeast Asians (SEA), and 382 (75.8%) Whites (W). There was a lower proportion of recipients ≥ 60 years of age in Asians than the Whites (SA 21%, EA/SEA 14%, W 34%, p =0.001). The proportion of donors ≥30 years of age was higher in the Asians compared to the Whites (SA 78.5%, EA/SEA 70%, W 61%, p =0.02). There was a lower proportion of MUD-HCT in the Asians (SA 21%, EA/SEA 30%, W 45%, p =0.009). The three groups were comparable regarding the recipient and donor sex, body mass index , and performance status. The proportion of SA with HCT-CI ≥ 3 was significantly higher (SA 50%, EA/SEA 37%, W 31%, p =0.002). There were fewer CMV 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 ATG/PTCy 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 ( p =0.8). Conclusion Our study confirms that South Asians aged ≥45 have worse survival after MAC-HCT. Supportive care is unable to overcome the differences in the outcomes. The high NRM is probably due to differences in comorbidities, frailty and pharmacogenetics and needs to be studied prospectively in multicenter studies.

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)0.002
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.243
Teacher spread0.233 · 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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Citations1
Published2025
Admission routes1
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