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Enregistrement W4405040041 · doi:10.1182/blood-2024-209364

Evaluating the Impact of Socioeconomic Disparities on Access and Outcomes of CAR T-Cell Therapy for Relapsed/Refractory B-Cell Lymphomas in Ontario, Canada

2024· article· en· W4405040041 sur OpenAlexaffabout
Karla Sanchez, Katrina Hueniken, S Osella Abate, Pablo Palomo Rumschisky, Carmel Waldron, Rachel Aitken, Anca Prica, Michael Crump, Vishal Kukreti, John Kuruvilla, Robert Kridel, Abi Vijenthira, Chloe Yang, Danielle Rodin, David Hodgson, Richard Tsang, Nauman Malik, Woodrow Wells, Christine I. Chen, Sita Bhella

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensUniversity Health NetworkPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésSocioeconomic statusMedicineRefractory (planetary science)B cellOncologyImmunologyEnvironmental healthPopulationAntibodyBiology

Résumé

récupéré en direct d'OpenAlex

Background Chimeric antigen receptor T-cell therapy (CAR-T) for relapsed/refractory large B-cell lymphoma (R/R LBCL) is now standard of care in Canada. Only a limited number of centres provide this therapy. There are limited data on disparities related to social determinants on outcomes and influence on access. Purpose The aim of this study was to assess the impact of socioeconomic status, as measured by the Ontario Marginalization Index (ON-Marg), on the likelihood of undergoing CAR-T after referral and treatment outcomes in patients with R/R LBCL. This was a retrospective review of patients 18 years or older with R/R LBCL referred from Ontario centres to Princess Margaret Cancer Centre (PM) between April 2020 and November 2023 for CAR-T therapy. The ON-Marg consists of four dimensions: material resources (MR), racialized and newcomer population (RN), age and labor force (AL), and household and dwellings (HD). Descriptive statistics were used to analyze baseline characteristics and the four dimensions of ON-Marg. Each dimension was divided into quintiles, ranging from 1 (low marginalization) to 5 (high marginalization). Need for interpreter at consent, rural vs urban setting, and median household income determined by national census data were also explored. The cohort was grouped into median household income tertiles of <$83000, $83000-104000 and >$104000, which were chosen to allow for similar size cohorts. Results We included 163 patients; 77% received CAR T-cell therapy while 23% did not. The median age was 60 years (range: 20-81), and 64% were male. The median follow-up was 17.51 months (95% CI 14.69-21.26). Our analysis showed no significant disparities in the likelihood of receiving CAR T-cell therapy across the quintiles of the four marginalization dimensions. In terms of the MR, 24% of the patients who underwent CAR T-cell treatment were allocated in quintile 1, 67% in quintiles 2-4, and 9% in quintile 5. Among the patients who did not, 22% were in quintile 1, 68% in quintiles 2-4, and 11% in quintile 5 (p=0.91). Similarly, in the RN dimension, 14% of the patients who received treatment were in quintile 1, 63% in quintiles 2-4, and 23% in quintile 5. Among those who did not receive it, 14% were in quintile 1, 62% in quintiles 2-4, and 24% in quintile 5 (p=0.98). In the AL dimension, quintile 1 accounted for 19% of the patients who received CAR T-cell therapy, quintiles 2-4 for 60%, and quintile 5 for 21%. For the patients who did not receive treatment, quintile 1 was 22%, quintiles 2-4 were 51%, and quintile 5 was 27% (p=0.66). For the HD dimension, quintile 1 consisted of 26% of patients who received treatment and 27% who did not, quintiles 2-4 had 56% and 41%, and quintile 5 had 17% and 32% respectively (p=0.11). No significant delays among marginalization groups were found in terms of the time from referral to the initial appointment (all p>0.05), the date of the initial appointment to the date of infusion (all p>0.05) or the date of progression to the date of infusion (all p>0.05). Of the 126 patients who received CAR T-cell therapy, there were 39 deaths from all causes, with a 12-month overall survival (OS) rate of 63.8%. OS outcomes did not show any significant differences between marginalization groups in any of the four dimensions that were evaluated (MR p=0.52, RN p=0.53, AL p=0.77, HD p=0.70). There were no significant OS differences between urban/rural patients (p=0.21), use of interpreter (p=0.42) or by income tertile (p=0.37). There were 74 events of progression or death, with a 12-month progression free survival (PFS) rate of 43.3%. Similar to the OS analysis, PFS did not reveal any significant differences among marginalization groups (MR p=0.077, RN p=0.62, AL p=0.81, HD p=0.51). There were no significant PFS differences between urban/rural patients (p=0.32), use of interpreter (p=0.37) or by income tertile (p=0.36). CRS and ICANS were observed in 86% and 29% of the patients. No significant differences in these toxicities were found when evaluated based on the different marginalization groups, income tertiles, use of interpreter and urban/rural status. Conclusions This study did not find any statistically significant evidence of an impact of socioeconomic status on the likelihood of receiving CAR T-cell therapy or on treatment toxicity or outcomes in a single payer universal health care system. A limitation requiring further analysis is only referred patients were included.

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 enseignants0,037
Score d'incertitude au seuil0,265

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0020,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,052
Tête enseignante GPT0,369
Écart entre enseignants0,317 · 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'admission2
Résumé présentoui

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