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Enregistrement W2782888072 · doi:10.1182/blood.v130.suppl_1.484.484

Assessment of Maintenance Rituximab after First-Line Bendamustine-Rituximab in Patients with Follicular Lymphoma: An Analysis from the BRIGHT Trial

2017· article· en· W2782888072 sur OpenAlexaffabout
Brad S. Kahl, John M. Burke, Richard van der Jagt, Julie Chang, Peter Wood, Tim E. Hawkins, David MacDonald, Judith Trotman, David Simpson, Kathryn S. Kolibaba, Samar Issa, Doreen M. Hallman, Ling Chen, Ian W. Flinn

Notice bibliographique

RevueBlood · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensQueen Elizabeth II Health Sciences CentreOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésRituximabVincristineBendamustineMedicineMaintenance therapyInternal medicinePrednisoneFollicular lymphomaCyclophosphamideSurgeryOncologyLymphomaGastroenterologyChemotherapy

Résumé

récupéré en direct d'OpenAlex

Introduction: Maintenance rituximab (R) has been shown to improve progression-free survival (PFS) in patients with follicular lymphoma (FL) after first-line R with cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) or R with cyclophosphamide, vincristine and prednisone (R-CVP) (PRIMA Trial). Whether a similar benefit is observed after first-line bendamustine-rituximab (BR) is unknown.The BRIGHT study investigated the safety and efficacy of BR versus R-CHOP or R-CVP in treatment-naive patients with indolent non-Hodgkin lymphoma or mantle cell lymphoma. Five-year follow-up data from this study has confirmed that duration of response and PFS were significantly improved in the BR treatment group. Overall survival (OS) was not statistically different between BR and R-CHOP/R-CVP. This ad hoc analysis examines the use of maintenance R in the BRIGHT study. Methods: The patient set used in this analysis consisted of 288 patients with FL who had a complete response (CR) or partial response (PR) based on the investigator9s assessment. The use of maintenance R was at the discretion of the investigator. Baseline characteristics were compared by treatment group for those patients receiving maintenance R versus those who did not receive maintenance R, as were the proportions of patients with CR and PR. Kaplan-Meier plots are presented for PFS and OS by treatment group subdivided by whether the patient received maintenance R. P values were determined by the log-rank test. Results: Among 144 patients with FL in the BR treatment group and 144 patients with FL in the R-CHOP/R-CVP treatment group, 81 (56%) and 83 (58%) received maintenance R, respectively. The baseline demographic and lymphoma characteristics of the patient groups are compared in the Table. In the BR treatment group, patients with B symptoms more commonly received maintenance R (38% vs 30%) while in the R-CHOP/R-CVP treatment group the opposite was true (29% vs 43%). In both treatment groups, patients with lactate dehydrogenase > 240 U/L and β2-microglobulin >3mg/L were less likely to receive maintenance R. In the BR treatment group, patients achieving CR to induction therapy were more likely to be assigned to no R maintenance (40% vs 22%; P= 0.0231). In the R-CHOP/R-CVP treatment group, the proportions with CR were similar in patients who did and did not receive maintenance R (19% vs 21%; P= 0.7636). Patients responding to BR (CR and PR) who received maintenance R had a significantly better PFS than responding patients who did not receive maintenance R; hazard ratio (HR) = 0.50 (95% confidence interval [CI] 0.26-0.94), P= 0.0295 (Figure). Patients responding to R-CHOP/R-CVP (CR or PR) who received maintenance R had a trend towards better PFS than responding patients who did not receive maintenance R; HR = 0.66 [95% CI 0.38-1.16], P= 0.1443. OS tended to be better in patients assigned to maintenance R (BR treatment group, HR = 0.39 [95% CI 0.14-1.05], P= 0.0537; R-CHOP/R-CVP group, HR = 0.32 (0.10-1.05; P= 0.0481). Conclusions: In this retrospective analysis of FL patients treated with BR induction therapy on the BRIGHT study, maintenance R significantly improved PFS with a trend towards improvement in OS, despite the fact that patients with CR were less likely to receive maintenance R. Maintenance R also showed a tendency towards improved outcomes after R-CHOP/R-CVP, consistent with data from randomized clinical trials (RCTs). Given that the application of maintenance R was based on investigator discretion, it is possible that the observed effect was due to confounding variables. However, the overall improvement in PFS in the maintenance R patients appears to be at least as great following BR as following R-CHOP/R-CVP and supports the notion of testing maintenance R after BR therapy in RCTs. Disclosures Kahl: Celgene: Consultancy; Gilead: Consultancy; ADC Therapeutics: Research Funding; Seattle Genetics: Consultancy; Genentech: Consultancy. Burke: Bayer: Consultancy; Celgene: Consultancy; Incyte: Consultancy; Gilead: Consultancy; Genentech: Consultancy. van der Jagt: Lundbeck, Teva: Consultancy; Teva: Research Funding. Wood: Bayer: Consultancy; Bayer, Boehringer Ingelheim, Bristol Myer Squibb: Honoraria. MacDonald: Lundbeck Canada, Roche Canada: Honoraria. Trotman: Janssen Cilag: Other: Funding facilitating research paid to third party (BioGrid Australia), Research Funding. Simpson: Amgen: Research Funding; Onyx: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Roche: Honoraria; Celgene: Honoraria, Other: travel expenses. Kolibaba: Celgene: Research Funding; Cell Therapeutics: Research Funding; Genentech: Research Funding; Gilead Sciences, Inc: Consultancy, Research Funding; Janssen: Research Funding; Novartis: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; TG Therapeutics: Honoraria, Research Funding; Acerta: Research Funding. Hallman: Teva Pharmaceuticals: Employment. Chen: Teva Pharmaceuticals: Employment. Flinn: Acerta: Research Funding; Novartis: Research Funding; Celgene: Research Funding; Pharmacyclics LLC: Research Funding; Trillium: Research Funding; Gilead: Research Funding; Pharmacyclics: Research Funding; Verastem: Research Funding; AbbVie Company: Research Funding; Merck: Research Funding; Janssen: Research Funding; Curis: Research Funding; Incyte: Research Funding; KITE: Research Funding; Constellation: Research Funding; Infinity: Research Funding; Agios: Research Funding; Beigene: Research Funding; Forty Seven: Research Funding; Genentech: Research Funding; Takeda: Research Funding; TG Therapeutics: Research Funding; Calithera: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Portola: Research Funding; Pfizer: Research Funding.

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)0,008
score de la tête « metaresearch » (Gemma)0,006
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,006
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
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 Opus0,009
Tête enseignante GPT0,258
Écart entre enseignants0,250 · 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

Citations20
Publié2017
Routes d'admission2
Résumé présentoui

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