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Record W4389248268 · doi:10.1182/blood-2023-174599

Trends in Allogeneic Stem Cell Transplantation for Good Risk Acute Myelogenous Leukemia in First Complete Remission: A Longitudinal Study of > 15 Years from the ALWP/EBMT

2023· article· en· W4389248268 on OpenAlexfundno aff
Arnon Nagler, Myriam Labopin, Urpu Salmenniemi, Depei Wu, Didier Blaise, Alessandro Rambaldi, Péter Reményi, Édouard Forcade, Régis Peffault de Latour, Patrice Chevallier, Peter A. von dem Borne, David Burns, Christoph Schmid, Johan Maertens, Nicolaus Kröger, Gesine Bug, Mahmoud Aljurf, Jan Vydra, Kazimierz Hałaburda, Fabio Ciceri, Mohamad Mohty

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
FundersServierIncyteJazz PharmaceuticalsAstellas PharmaAlexion PharmaceuticalsMallinckrodt PharmaceuticalsGilead Sciences
KeywordsMedicineInternal medicineNPM1TransplantationCumulative incidenceProportional hazards modelLeukemiaHematopoietic stem cell transplantationGastroenterologyOncologyKaryotype

Abstract

fetched live from OpenAlex

Background: Favorable acute myelogenous leukemia (AML) includes AML with t (8:21), inv (16), and those with NPM1 without FLT3- ITD without adverse cytogenetics (ELN 2022). The incidence of relapse (RI) in favorable-risk AML with chemotherapy is 35%-40%. Although RI is ~20% lower with allogeneic transplantation (HSCT), transplantation is usually not indicated in favorable risk AML at first complete remission (CR1) due to transplant-related mortality. However, in recent years, HSCTs have been associated with significantly lower non relapse mortality (NRM) and better outcomes. Methods:Our aim was to assess outcomes of HSCT in favorable risk AML (t (8:21), inv (16), and NPM1 mutFLT3 WT) in CR1, comparing 3 time periods: 2005-2009, 2010-2014, and 2015-2021. Statistical tests included a multivariate analysis (MVA) adjusting for potential confounding factors performed using a Cox proportional-hazards regression model for main outcomes. Results: 1850 patients (pts) were included, 526 with t (8:21), 625 with inv (16), and 699 with NPM1 mutFLT3 WT (normal karyotype). 222 pts were transplanted in 2005-2009, 392 in 2010-2014, and 1236 in 2015-2021. As the follow-up period differed, being 103.1 (IQR, 92.1-114.0), 78.3 (IQR, 69.4-86.3), and 32.0 (IQR, 29.5-34.5) months, respectively (p<0.0001), all survival events were censored at 3 y. Pts undergoing HSCT in 2015-2021 were older, with a median age of 50.9 (range 18.2-76.4) vs40.4 (range 18.3-67.7) and 42.4 (range 18.4-71) y, in those transplanted in 2005-2009 and 2010-2014, respectively (p<0.0001). More pts >50 y of age were transplanted in the latest period with 52.7% vs the 2 earlier periods 27.9% and 32.1% (p<0.0001) and figures for >60 y were 25.4% vs 8.1% and 11% (p<0.0001), respectively. In 2005-2009 the most frequent diagnosis was t (8:21) at 44.6%, while in 2015-2021, it was NPM1 mutFLT3 WT at 45.6% (p<0001). In 2005-2009, the most frequent donors were matched siblings (MSD) (63.1%), while in 2015-2021 they were unrelated (UD) (50.7%). Haploidentical (haplo) transplants increased from 5.9% to 14.5% (p<0.0001). Bone marrow grafts decreased from 24.8% to 13.2%, while peripheral blood (PB) grafts increased from 75.2% to 86.8% (p<0.0001). Conditioning was myeloablative in 69.8%, 64.8%, and 60.2% and was reduced intensity in 30.2%, 35.2%, and 39.8% in pts transplanted in 2005-2009, 2010-2014, and 2015-2021 (p=0.014). Graft-versus-host disease (GVHD) prophylaxis with in vivo T cell depletion or post-transplant cyclophosphamide (PTCy) was more frequent in 2015-2021 compared to the other two periods (p<0.0001). Day 60 engraftment (ANC >0.5 x 10 9/L) was 98.2 % vs 98.4% vs 98.5% (p=0.17). Day 180 incidence of acute (a) GVHD grade II-IV was 18.2%, 21.1%, and 21.6%; grade III-IV was 5.1% 5.7%, and 7.3% while the incidence of 3-y chronic (c) GVHD was 49.3%, 50.4% and 39.2%. On MVA the incidence of total cGVHD was reduced in HSCTs performed >2015 compared to those performed in 2005-2009, hazard ratio (HR) =0.74 (95% CI 0.56-0.99, p=0.046) and GVHD-free, relapse-free survival (GRFS) improved for pts transplanted from 2010-2014 vs those transplanted in 2005-2009, HR=0.74 (95% CI 0.56-0.98, p=0.037) (Figure 1). All other HSCT outcome parameters including NRM, RI, leukemia-free survival (LFS), and overall survival (OS) did not differ (Figure 1) with no improvement >2015 compared to 2010-2014 (Figure-1 A). LFS, OS, and GRFS were superior in pts with t (8:21) with HR=1.32 (95% CI 1.03-1.68, p=0.026), HR=1.38 (95% CI 1.04-1.83, p=0.027) and HR=01.25 (95% CI 1.02-1.53, p=0.035), respectively. Other poor prognostic factors were older pt age (by 10 y) for NRM and OS; 10/10 and 9/10 UD vs MSD for aGVHD II-IV (9/10 also for III-IV aGVHD) and haplo vs MSD for NRM, OS, aGVHD II-IV, and total cGVHD. The combination of female donor to male pt was a poor prognostic factor for NRM, OS, GRFS, and cGVHD. In vivo T cell depletion was a positive prognostic factor for GRFS and reduced incidence of aGVHD and cGVHD. PTCy was associated with a lower incidence of cGVHD and PB grafts with an increased risk of total cGVHD. Conclusions: In this retrospective analysis of HSCT in pts with favorable risk AML in CR1, transplanted over 16 years, we observed an increased number of transplants in pts >60y, from UD and haplo with PB grafts and in vivo T cell depletion or PTCy as GVHD prophylaxis. Most importantly, 3-y GRFS improved >2010 and total cGVHD reduced >2015, while other HSCT outcome parameters have not changed.

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)0.001
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.310
Teacher spread0.255 · 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
Published2023
Admission routes1
Has abstractyes

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