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Enregistrement W3111405919 · doi:10.1182/blood-2020-140585

Frailty Is Associated with Increased One-Year Mortality in Patients with Newly Diagnosed Diffuse Large-B-Cell Lymphoma: A Population-Based Study

2020· article· en· W3111405919 sur OpenAlexaffabout
Abi Vijenthira, Lee Mozessohn, Chenthila Nagamuthu, Ning Liu, Danielle Blunt, Shabbir M.H. Alibhai, Anca Prica, Matthew C. Cheung

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensUniversity Health NetworkInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineDiffuse large B-cell lymphomaPopulationProportional hazards modelInternal medicineRetrospective cohort studyEmergency departmentLymphoma

Résumé

récupéré en direct d'OpenAlex

Introduction: Previous studies have demonstrated that frailty is associated with mortality among patients with non-Hodgkin lymphoma including diffuse large B-cell lymphoma (DLBCL). However, no studies have examined frailty in an unselected population-based sample of patients with DLBCL, nor have data on health care utilization been considered as a potential mediator of this relationship. Objective: To determine whether frailty is associated with one-year survival in an unselected population of patients with DLBCL, and examine whether its impact is mediated by health care utilization during chemotherapy treatment. Methods: A retrospective cohort study was conducted using population-based health care data in Ontario, Canada. Patients >65 years diagnosed with DLBCL or transformed follicular lymphoma between January 2006 and December 2017 and receiving first-line chemo-immunotherapy were included. Frailty was defined by modifying a previously validated score developed for use with population-based data in Ontario, comprising 30 multidimensional variables (McIsaac, Ann Surg. 2019;270(1):102-108). Patients were categorized as "frail" (score >0.21) vs. "non-frail" (score ≤0.21). Covariates included age, number of comorbidities based on the Johns Hopkins Aggregated Diagnosis Groups (ADGs), and health care utilization during chemotherapy (defined as emergency department (ED) visit or inpatient hospitalization not resulting in death during treatment, and analysed as a time-varying covariate). Cox regression was performed to examine the association between frailty and one-year mortality (primary outcome). Secondary outcomes included health care utilization, chemo-immunotherapy exposure, and cause of death. Results: 5,527 patients were included in the study. 5,216 patients (94%) had de novo DLBCL, and 311 (6%) of patients had transformed follicular lymphoma. The median age was 75 years (IQR 70-80), and 48% (N=2672) were female (Table 1). 2,699 (49%) of patients were classified as frail (Table 2). Frail patients tended to be older (median age 76 (IQR 71-81) vs. 74 years (IQR 70-79)). The difference in mortality between frail and non-frail patients was most pronounced in the initial year following start of treatment (Figure 1a). Within 90 days of first-line rituximab, 14% (N=370) of frail vs. 7% (N=185) of non-frail patients had died (p<0.0001). Within one-year of first-line treatment, 32% (N=868) of frail patients had died compared to 20% (N=553) of non-frail patients (unadjusted HR 1.8, 95% CI 1.6-2.0, p<0.0001, Figure 1b). Among frail patients who died within 1 year (N=868), 34% (N=298) had only received 1 cycle of chemotherapy. In multivariable modelling controlling for age, number of ADG comorbidities, and health care utilization during chemotherapy, frailty (binary exposure) remained independently associated with one-year mortality (adjusted HR 1.6, 95% CI 1.5-1.8, p<0.0001). The relationship between frailty and survival remained consistent when measured in quartiles (HR 1.6 (95% CI 1.3-1.9) for Q2, 2.0 (95% CI 1.7-2.4) for Q3, 2.7 (95% CI 2.3-3.2) for Q4, p<0.0001, Figure 1b). Frail patients were significantly more likely to receive only 1 cycle of chemotherapy than non-frail patients (14% vs. 7%, p<0.0001). Frail patients also had higher health care utilization during chemotherapy (mean ED visits 0.75 + 1.47 vs. 0.59 + 1.17, p<0.001; mean hospitalizations 0.9 + 1.12 vs. 0.72 + 1.05, p<0.001). Frail patients were also more likely to die of DLBCL (38.3 vs. 29%, p<0.0001). Conclusion: Frailty is significantly associated with one-year mortality in patients with newly diagnosed DLBCL, even after adjusting for age, comorbidities, and health care utilization. Frailty appears to be associated with poor tolerability of chemotherapy and a higher likelihood for requiring acute hospital-based care, and future analyses will explore whether this is related to patients suffering increased treatment-related toxicity. Future analyses of these data will also address whether frail patients who die within one-year of first-line treatment have different clinical characteristics compared to frail patients who survive beyond one year. Future prospective studies may help clinicians understand whether any frailty-related variables are modifiable and the role of alternative treatment strategies for vulnerable patients. Disclosures Prica: astra zeneca: Honoraria; seattle genetics: Honoraria; Gilead: Honoraria.

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 enseignants0,097
Score d'incertitude au seuil0,193

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
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 Opus0,018
Tête enseignante GPT0,243
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

Citations2
Publié2020
Routes d'admission2
Résumé présentoui

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