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Enregistrement 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 sur 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

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensnon disponible
Organismes subventionnairesServierIncyteJazz PharmaceuticalsAstellas PharmaAlexion PharmaceuticalsMallinckrodt PharmaceuticalsGilead Sciences
Mots-clésMedicineInternal medicineNPM1TransplantationCumulative incidenceProportional hazards modelLeukemiaHematopoietic stem cell transplantationGastroenterologyOncologyKaryotype

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,310
Écart entre enseignants0,255 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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