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Enregistrement W4405043763 · doi:10.1182/blood-2024-203896

Frailty and Long-Term Survival Among Older Adults with Blood Cancers

2024· article· en· W4405043763 sur OpenAlexaff
Clark DuMontier, Angel M. Cronin, Tammy T. Hshieh, Ameya Sanyal, Michelle Fredericks, Nicholas Groblewski, Lee Mozessohn, Daniel J. DeAngelo, Richard M. Stone, Robert J. Soiffer, Jane A. Driver, Gregory A. Abel

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensHealth Sciences CentreSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineGerontologyHematologic NeoplasmsTerm (time)CancerInternal medicineOncology

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Formal geriatric assessment is now recommended for all older (65+) adults with cancer who are receiving systemic therapy (Dale, JCO, 2023). While many tools have been validated in cancer populations, the association of geriatrics-driven frailty assessment tools with long-term survival for older adults with hematologic malignancies has yet to be characterized. METHODS: The Older Adult Hematologic Malignancy (OHM) Program at Dana-Farber Cancer Institute aims to assess the utility of three widely-studied frailty assessment tools-deficit accumulation method, phenotypic model and 4-meter gait speed (4MGS)-for older adults with blood cancers. From February 2015 to July 2024, we approached patients aged ≥ 73 years presenting for an initial consultation for MDS/leukemia, myeloma, or lymphoma at our institution. A trained research assistant conducted a frailty assessment consisting of 42 patient-reported and objective measures, spanning domains of comorbidity, functional status (e.g., instrumental activities of daily living, IADLs), physical performance (e.g., 4MGS and grip strength), and cognition (e.g., delayed recall). The majority of assessments were performed in-person, with a portion conducted virtually (DuMontier, Blood Advances, 2022). The deficit accumulation method (Rockwood, Journals of Gerontology, 2007) counts aging-related health deficits across multiple domains to compute a frailty index (FI) as the proportion of deficits present out of the total number of possible deficits measured. Patients were classified as robust if the FI was less than 0.2, pre-frail if between 0.2 and 0.35, and frail if greater than 0.35. The phenotypic model (Fried, Journals of Gerontology, 2001) uses five criteria to define a frailty syndrome (slow gait speed, weakness measured by grip strength, self-reported exhaustion, low physical activity, and weight loss). Patients were classified as robust if they had no deficits, pre-frail if they had one or two deficits, and frail if they had 3 or more deficits. For gait speed, patient's normal 4MGS was analyzed as a categorical variable (>0.8, >0.6 to 0.8, < 0.6) consistent with a priori cutoffs from the literature. Patients were followed from the time of initial consultation through the date of death or last follow-up, after which they were censored. Demographic and clinical variables were descriptively summarized, and multivariable Cox proportional hazards regression was used to estimate hazard ratios for the frailty assessment tools adjusted for age and gender. RESULTS: As of July 18, 2024, frailty was assessed for 1271 patients; all three measures of interest-deficit accumulation method, phenotypic model and 4MGS-were available for 945 patients. Among these, median age was 78 years (IQR, 76 to 82) and 36% were female. 32% had MDS/AML, 34% lymphoma, and 34% myeloma. Median follow-up was 30 months (IQR, 10 to 59) among all patients and 44 months (IQR, 0.03 to 110) among 490 patients alive at last follow-up. Median survival was 56 months and 5-year overall survival was 48% (95% CI 45%, 52%). According to the deficit accumulation method, 34% were pre-frail and 8% were frail. According to the phenotypic model, 61% were pre-frail and 6% were frail. In terms of 4MGS, 33% were >0.6 to 0.8 m/s and 14% were < 0.6 m/s. Over half (52%) reported weak grip strength, 31% unintentional loss of at least 10 pounds within the past year, and 10% “exhaustion.” All three frailty assessment tools were associated with mortality, independent of age and gender (deficit accumulation method: robust ref; pre-frail HR 1.90 [95% CI 1.55, 2.32]; frail HR 2.46 [1.83, 3.31]; phenotypic model: robust ref; pre-frail HR 2.07 [1.64, 2.62], frail HR 3.19, [2.19, 4.66]); 4MGS: >0.8 m/s ref; >0.6 to 0.8 HR 1.47 [1.19, 1.81], ≤ 0.6 HR 2.13 [1.63, 2.78]). CONCLUSIONS: In this large cohort of older adults with blood cancers and long-term follow-up, pre-frail and frail states were prevalent as measured with gold standard geriatric tools. Impaired mobility, weakness, and weight loss were more prevalent than in general populations of community-dwelling adults (e.g., 31% of OHM patients reported weight loss, while this has been found to be only 6% in a general population; Fried, Journals of Gerontology, 2001). All three tools showed dose-response relationships with survival. These data underscore the importance of measuring and addressing frailty in older adults undergoing treatment for blood cancer.

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)0,003
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 enseignants0,005
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
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 ouverte0,0000,001
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 Opus0,012
Tête enseignante GPT0,256
Écart entre enseignants0,244 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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