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Enregistrement W4417002266 · doi:10.1182/blood-2025-4430

Frailty as a prognostic factor in younger adult patients with Acute Myeloid Leukemia undergoing allogeneic stem cell transplantation

2025· article· en· W4417002266 sur OpenAlexaff
Sergio Rodríguez‐Rodríguez, Nihar Desai, Maria Queralt Salas Gay, Eshrak Al‐Shaibani, Igor Novitzky‐Basso, Arjun Law, Auro Viswabandya, Fotios V. Michelis, Jonas Mattsson, Dennis Kim, Rajat Kumar, Tommy Alfaro Moya

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésCumulative incidenceHazard ratioProportional hazards modelHematopoietic stem cell transplantationTransplantationMyeloid leukemiaIncidence (geometry)Confidence interval

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Acute myeloid leukemia (AML) is potentially curable with allogeneic stem cell transplantation (alloHCT); however, its curative potential is often limited by treatment-related toxicity. We have previously demonstrated that frailty independently predicts both overall survival (OS) and non-relapse mortality (NRM) in all patients undergoing alloHCT. In this study, we aimed to evaluate the prognostic impact of frailty in patients under the age of 65 with AML undergoing alloHCT. Methods We retrospectively analyzed 213 patients aged <65 years who underwent alloHCT between 2014 and 2025. The previously validated Hematopoietic Cell Transplantation Frailty Scale (HCT-FS) was used, comprising eight domains: Clinical Frailty Score (≥3 vs. not frail), instrumental activities of daily living (IADL; ≥1 limitation vs. no limitation), timed up and go test (TUGT; >10s vs. <10s), grip strength (<16 kg for females and <26 kg for males vs. higher), self-rated health (SRH; fair or poor vs. normal), history of falls in the last 6 months (yes vs. no), serum albumin (<38 g/L vs. >38 g/L), and C-reactive protein (CRP ≥11 mg/L vs. <11 mg/L). Patients were stratified into three groups: fit (≤1), pre-frail (>1 to ≤5.5), and frail (≥5.5). Primary endpoints were NRM and OS. Secondary endpoints included cumulative incidence of relapse (CIR), relapse-free survival (RFS), and graft-versus-host disease-free, relapse-free survival (GRFS). Kaplan-Meier estimates were calculated for OS and RFS, with comparisons via the log-rank test. Cox proportional hazards models were used for prognostic analysis, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). For cumulative incidence of CIR and NRM, the Fine-Gray method and Gray’s test were applied considering competing risks. Results The median age of the cohort was 53 years (range 18–64), with 54% (n=115) female. At pre-transplant assessment, 13.2% (n=28/212) had a high/very high disease-risk index, 30.1% (n=63/209) had an HCT-CI ≥3, and 13.4% (n=28/208) had a Karnofsky Performance Status (KPS) <90. Based on the HCT-FS, 33.3% (n=71/213) were classified as fit, 52.1% (n=111) as pre-frail, and 14.6% (n=31) as frail. Fifty-five percent (n=119) received myeloablative conditioning. Donor sources included matched unrelated (61%, n=130), matched sibling (23.9%, n=51), and haploidentical donors (15%, n=32). Most patients (88.7%) received post-transplant cyclophosphamide-based GvHD prophylaxis. After a median follow-up of 43.4 months [range 35.7–47.9], the 1- and 2-year NRM for the entire cohort were 13.4% [9.2–18.4] and 15.2% [11.5–21.6], respectively. When stratified by HCT-FS, fit patients had a 1- and 2-year NRM of 2.9% and 4.4%, pre-frail patients 14.0% and 15.1%, and frail patients 35.5% and 46.5% (p<0.001). Compared to fit patients, pre-frail patients had a trend toward higher NRM (HR 2.82 [0.97–8.21], p=0.057), while frail patients had a significantly higher risk (HR 2.36 [1.97–5.73], p<0.001). The 1- and 2-year OS for the full cohort were 79.7% [73.6–84.6] and 70.8% [63.9–76.7], respectively. Fit patients had a 1- and 2-year OS of 91.4% and 86.8%, pre-frail 78.2% and 69.3%, and frail 58.1% and 40.1% (p<0.001). Compared to fit patients, frail patients had a significantly higher risk of death (HR 1.88 [1.36–2.62], p<0.001), while pre-frail patients also had decreased survival though not statistically modelled here. The 1-year CIR for the cohort was 14.5% [10.1–19.7], with no significant differences between fit, pre-frail (p=0.59), or frail (p=0.21) groups. Similarly, the 1- and 2-year RFS were 84% [77.9–88.6] and 73.3% [65.8–79.4], with no significant differences between fit, pre-frail (p=0.36), or frail (p=0.54) patients. The 1- and 2-year GRFS were 53.2% [46.1–59.7] and 41.2% [34.2–48.0]. Fit patients had a GRFS of 59.6% and 51.0%, pre-frail 55.0% and 39.4%, and frail 32.3% and 25.1% (p=0.002). Compared to fit patients, frail patients had a significantly higher risk of inferior GRFS (HR 1.52 [1.17–1.98], p=0.001). Conclusion Frailty was a predictor of both OS and NRM even in young patients with AML undergoing alloHCT. Our results support the growing evidence that physiological reserve may be a more meaningful determinant of transplant fitness than chronologic age alone. Routine use of the HCT-FS could better inform treatment decisions, optimize eligibility criteria, and identify patients who might benefit from prehabilitation or modified conditioning approaches.

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)0,000
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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
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 savante0,0000,000
Science ouverte0,0000,000
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 Opus0,009
Tête enseignante GPT0,234
Écart entre enseignants0,225 · 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'admission1
Résumé présentoui

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