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Enregistrement W4389247977 · doi:10.1182/blood-2023-180088

Real-World Response Rates across Lines of Therapy Among Patients with Relapsed/Refractory Follicular Lymphoma

2023· article· en· W4389247977 sur OpenAlexaff
Tycel Philips, Laurie H. Sehn, Anthony Wang, Junhua Yu, Elizabeth Marchlewicz, Rajesh Kamalakar, Kavita Sail, Donald Arnette, Shibing Yang, Alex Mutebi, Fernando Rivas Navarro, Gilles Salles

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSpinal Cord Injury BCUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésObinutuzumabMedicineBendamustineRituximabInternal medicineFollicular lymphomaChemoimmunotherapyOncologySalvage therapyVincristineChlorambucilLenalidomideMaintenance therapySurgeryLymphomaCyclophosphamideChemotherapyMultiple myeloma

Résumé

récupéré en direct d'OpenAlex

Background: Follicular lymphoma (FL) is the most common indolent subtype of non-Hodgkin lymphoma. Despite the generally indolent nature of the condition, there are subgroups of FL patients who may not have an indolent experience, with their disease not responding to multiple lines of therapy. Thus, optimization of novel therapy could improve patient outcomes. This analysis examined treatment patterns, overall response rates (ORR), and complete response (CR) rates by line of therapy (LOT) in relapsed/refractory (R/R) FL in third-line or later (3L+) therapy. Methods: This retrospective observational study was conducted using the COTA database, comprising electronic health records (EHR) from academic (50%) and community (50%) practices in the US. Adults with a confirmed diagnosis of FL, with 3L+ therapy initiation in 2010 or later, at least 3 months of follow-up, and any response assessment after 3L initiation were included. LOTs eligible for inclusion met the following conditions: treatment with an anti-CD20, alkylating agent, or lenalidomide, and no investigational drug in the selected LOT. In addition, patients with only 3L had that line selected; patients with more than 1 eligible LOT had only 1 LOT randomly selected for the analysis. Outcomes were assessed by FL International Prognostic Index (FLIPI), double refractory (DR) status, and type of therapy. DR status was defined as being refractory (disease progression or initiation of a new LOT in <6 months) to an anti-CD20 monoclonal antibody therapy and an alkylating agent. Chemoimmunotherapy (CIT) regimens included obinutuzumab/rituximab + bendamustine (OB/BR), rituximab/obinutuzumab+cyclophosphamide, doxorubicin, vincristine, and prednisolone (R/O+CHOP), R/O+CVP, R/O+other alkylating agent, and R/O+fludarabine and cyclophosphamide (FC). Novel therapies included lenalidomide + rituximab (R 2), phosphatidylinositol-3-kinase (PI3K) inhibitors, and chimeric antigen receptor T-cell therapy (CAR T). The proportion of patients with CR (as retrieved from clinician documentation in EHR) as the physician-reported response within each LOT was calculated. ORR was calculated as the proportion of patients with a CR or PR. Response rates were reported by LOT (3L, 4L, 5L+) and stratified by patient age (<65 vs ≥65 years), FLIPI score (low/intermediate vs high), and DR (not DR vs DR). Results: Overall, 240 patients with R/R 3L+ FL were included: 3L (n=140), 4L (n=55), and 5L+ (n=45). At 3L initiation, median age was 66 years and most patients were male (58.8%), White (89.6%), and had FL grade 1/2 (72.5%); 47.9% of patients were DR, 22.9% had novel therapy use, and 51.3% had CIT use. A total of 152 patients had FLIPI scores available, 38.2% of whom had high-risk scores at 3L initiation. Among all R/R 3L+ FL patients, ORR was 67.9%, with CR in 30.4% ( Figure 1). Response rates decreased across LOTs for both ORR (3L: 72.9%; 4L: 65.5%; 5L+: 55.6%) and CR (3L: 35.0%; 4L: 30.9%; 5L+: 15.6%) ( Figure 2). ORR was higher among all R/R 3L+ FL patients <65 years vs ≥65 years (75.5% vs 62.3%), as was CR rate (36.3% vs 26.1%). Among the subset of R/R 3L+ FL patients with a FLIPI score, those with low/intermediate risk vs high risk had a greater ORR (78.7% vs 58.6%) and CR rate (36.2% vs 20.7%). ORR was also higher for R/R 3L+ patients without DR status vs with DR status (73.0% vs 63.2%), as was CR rate (37.4% vs 24.0%). Patients receiving novel therapy 3L+ had an ORR of 70.9% and a CR rate of 27.3%. Among R/R 3L+ FL patients, more LOTs of CIT were associated with decreased response rates; patients with CIT in 1 LOT vs CIT in >2 LOTs had greater ORR (76.4% vs 69.2%) and CR rate (37.4% vs 26.9%). Conclusions: This analysis provides granular details on patients with R/R FL with poor prognosis. Patients with FL who progress to later LOTs have worsening response rates. This analysis included more patients with later LOTs than previously published reports, providing additional insight into response rates as treatment progresses. Lower response rates were observed among patients ≥65 years, those with a high-risk FLIPI score, and those with DR status. The high utilization of CIT in later LOTs, despite suboptimal response rates, especially among subgroups of patients with FL who may not have indolent experience of the disease, underlines the need for efficacious alternative therapies in patients with R/R 3L+ FL.

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)0,002
score de la tête « metaresearch » (Gemma)0,009
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 seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
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 Opus0,015
Tête enseignante GPT0,282
Écart entre enseignants0,267 · 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

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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