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Enregistrement W4417016416 · doi:10.1182/blood-2025-2652

Frailty assessed by HCT-FS predicts survival and non-relapse mortality across hematologic disorders undergoing allo-HCT: A multicenter Study of 992 patients

2025· article· en· W4417016416 sur OpenAlexaffabout
Tommy Alfaro Moya, Maria Queralt Salas Gay, Ivan Pašić, Monica Baile Gonzalez, Marina Acera Gómez, Andrés Sánchez‐Salinas, Joaquina Salmerón Camacho, Verónica Illana Álvaro, Zahra Abdallah-Lefdil, Javier Cornago Navascués, Laura Pardo Gambarte, Sara Fernández‐Luis, Libia Vega, Sara Villar, Patricia Beorlegui Murillo, Albert Esquirol, Isabel Izquierdo García, Alberto Mussetti, Esperanza Lavilla, Javier López, Silvia Filafferro, Pascual Balsalobre, Leyre Bento De Miguel, Fotios V. Michelis, Auro Viswabandya, Jonas Mattsson, Shabbir M.H. Alibhai, Montserrat Rovira, Dennis Kim, Anna Sureda Balarí, Rajat Kumar

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMyelodysplastic syndromesTransplantationHematopoietic stem cell transplantationMulticenter studyObservational studyMyeloid leukemiaAcute leukemiaDiseaseHematopoietic cell

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Frailty is an important factor impacting outcomes following allogeneic hematopoietic cell transplantation (allo-HCT). The Hematopoietic Cell Transplantation Frailty Scale (HCT-FS) has been validated as a reliable predictor of transplant-related outcomes in general transplant populations (Ref:BMT2023;58:317-324). However, its prognosis value within specific disease subgroups, including non-malignant disorders, remains less defined. The aim of this multicenter study was to assess the utility of the HCT-FS as a predictor of overall survival (OS) and non-relapse mortality (NRM) across different hematologic disorders in undergoing allo-HCT. Methods: This observational multicenter study was conducted between 2018 to 2023 across sixteen transplant centers (1 in Canada and 15 in Spain). Frailty was prospectively assessed using the HCT-FS during the initial transplant consultation. Based on this tool, patients were classified as fit, pre-frail, or frail. The primary endpoints were OS and NRM. Kaplan-Meier estimates were used for survival analyses, and differences between frailty subgroups were assessed using the log-rank test. Results: A total of 992 adults with a median age of 56 years (range 18-75) were included. Diagnoses included acute myeloid leukemia (AML, n=498), myelodysplastic syndromes (MDS, n=168), acute lymphoblastic leukemia (ALL, n=113), myeloproliferative neoplasms (MPN, n=92), other lymphoid malignancies (LM, n=73), and non-malignant diseases (n=48). Among the cohort, 41.1% were female, 18.1% had an HCT-CI >3, and 25.2% had a KPS <80%. Reduced-intensity conditioning was used in 61.8% of patients; 76.4% received post-transplant cyclophosphamide; 71.2% received HLA-matched donor grafts, 10.4% from 9/10 mismatched unrelated donors, and 18.3% from haploidentical donors. Frailty assessment revealed 318 (32.1%) fit, 543 (54.7%) pre-frail, and 131 (13.2%) frail patients. The prevalence of frailty differed across disease groups (p=0.002), with patients with MPN showing the lowest incidence (3.3%) compared to those with AML, MDS, ALL, LM and non-malignant diseases (15%, 10.7%, 15%, 15.1% and 14.6% respectively). Likewise, the proportion of fit patients also varied among disease groups, with MDS and MPN patients showing the highest proportions (42.3% and 45.7%, respectively). Overall, frailty, as defined by HCT-FS, was significantly associated with worse OS (2-year OS: 78.9% fit, 66.0% pre-frail, 51.7% frail; p<0.001), primarily due to increased NRM (2-year NRM: 10.5%, 18.9%, and 33.2%, respectively; p<0.001). The cumulative incidence of relapse was similar across groups (2-year CIR: 19.0%, 23.5%, and 22.3%; p=0.229). The negative impact of frailty on OS and NRM was consistent across disease groups, although statistical significance varied. In AML, OS was significantly lower in frail patients than in fit and pre-frail ones (2-y: 54.9%, 78.3% and 66.9%, P<0.001), with higher NRM (33.3% vs. 11.0% and 17.1%; p<0.001). In ALL, OS was higher in fit patients than in pre-frail and frail ones (2-y: 87.9%, 64.0% and 52.3% (p=0.032), with NRM rates of 3.0%, 20.2% and 23.5%, respectively (p=0.147). Among LM patients, OS was significantly higher in fit patients than in pre-frail and frail ones (2-y: 73.5%, 45.5% and 27.3%, p=0.021), with NRM rates of 38.6%, 27.5%, and 15.4% (p=0.27). In MDS, OS tent to be higher in fit patients than in pre-frail and frail ones (2-year: 71.6%, 60.4% and 42.8%, p=0.158), while NRM increased with frailty (12.5%, 24.2%, and 39.3%; p=0.051). The incidence of frailty among MPN patients was low (3.3%), likely reflecting stricter patient selection. However, frail MPN patients still had lower OS (2-year: 66.7%) and higher NRM (33.3%) compared to fit (80.5%, 14.7%) and pre-frail (78.4%, 19.4%) patients (p=0.187 and p=0.083). Finally, patients with non-malignant diseases had excellent outcomes overall. However, OS was lower in frail patients (71.4%) compared to pre-frail (79.7%) and fit individuals (100%) (p=0.152), reinforcing the clinical significance of its assessment in this transplant setting. Conclusions: This study sustains that frailty, as assessed by the HCT-FS, is a strong predictor of OS and NRM following allo-HCT, independent of the underlying hematologic disease. Its adverse prognostic impact is consistent across diagnoses and support the incorporation of frailty assessment into clinical practice to improve risk stratification and clinical decision-making.

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)0,002
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 enseignants0,009
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
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 Opus0,017
Tête enseignante GPT0,310
Écart entre enseignants0,293 · 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
Publié2025
Routes d'admission2
Résumé présentoui

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