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Record W4407977037 · doi:10.1016/j.jtct.2025.01.097

Impact of Graft Versus Host Disease Following Allogeneic Hematopoietic Cell Transplantation on Leukemia Free Survival in Hematologic Malignancies: A CIBMTR Analysis

2025· article· en· W4407977037 on OpenAlexaff
Andrea Bauchat, Andrew C. Peterson, Kwang Woo Ahn, Parinda A. Mehta, Christine L. Phillips, Kirk R. Schultz, Akshay Sharma, Larisa Broglie, Muna Qayed

Bibliographic record

VenueTransplantation and Cellular Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsHematopoietic cellLeukemiaHematopoietic stem cell transplantationDiseaseTransplantationMedicineGraft-versus-host diseaseHematologic diseaseHaematopoiesisHematologic NeoplasmsImmunologyOncologyBiologyInternal medicineStem cellGenetics

Abstract

fetched live from OpenAlex

Relapse is a leading cause of mortality in children after hematopoietic cell transplantation (HCT) for hematologic malignancies . Graft-versus-host disease (GVHD) has been reported as protective against relapse. We aimed to evaluate impact of acute and chronic GVHD on relapse in children with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) adjusted for disease risk. We included patients <18 years who received first allogeneic HCT for ALL (n=1292) or AML (n=1036) following myeloablative conditioning between 2008-2017. We assigned the pediatric disease risk index (pDRI) which encompasses age, disease status including MRD and cytogenetics (for AML), and is validated for disease-free survival (DFS): AML was categorized as low/intermediate (63%), and high/very high risk (28%), and ALL as low (36%), intermediate (58%), and high risk (2%). Most patients had pre-HCT comorbidity index 0-2 (83% AML/ALL), received unrelated donor (AML 39%, ALL 34%) and bone marrow grafts (AML 53%, ALL 48%). The predominant GVHD prophylaxis was calcineurin inhibitor/methotrexate (AML 46%, ALL 45%). Chronic GVHD was included as a time-varying covariate while aGVHD was analyzed at the day 100 landmark, excluding patients who relapsed, died, or had cGVHD prior. At day 100, 47% of AML patients developed aGVHD: grade I (17%), grade II (19%), grade III (8%) and grade IV (3%), and 27% had cGVHD: limited (10%) and extensive (17%). In ALL patients, 52% had aGVHD: grade I (17%), grade II (21%), grade III (9%) and grade IV (4%), and 30% developed cGVHD: limited (11%), extensive (19%). On univariate analysis , cumulative incidence of relapse at 6 months, 1 year, and 2 years was not statistically different among patients with no aGVHD and any aGVHD grade (adjusted DFS and relapse shown in figure 1 ). Both cohorts had inferior DFS and overall survival (OS), with higher non-relapse mortality (NRM) among those with grades III-IV aGVHD. In multivariate analysis including disease and HCT characteristics, neither aGVHD nor cGVHD had significant impact on relapse for AML (p=0.59 and 0.85 respectively) or ALL (p= 0.48 and 0.61 respectively). pDRI categories for AML high/very high, ALL intermediate, and ALL high risk had increased risk of relapse (HR 1.87, p<0.0001, HR 1.54, p=0.0029, and 3.45, p=0.0078 respectively). Extensive cGVHD and severe aGVHD grades III-IV had increased NRM for both cohorts. Grade IV aGVHD was associated with worse DFS in AML (HR 2.24, p=0.004) and ALL (HR 1.79, p=0.014) and aGVHD grades II-IV had worse OS. cGVHD was not associated with DFS or OS. For AML, pDRI high/very high had worse OS. For ALL, pDRI intermediate and high risk pDRI had worse OS. We conclude that neither acute nor chronic GVHD portend protection against relapse when the aggressiveness of the underlying disease is adjusted for with the pDRI. Conversely, severe grades of GVHD are associated with increased NRM and inferior overall survival .

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)0.003
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractno

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