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Enregistrement W4405047353 · doi:10.1182/blood-2024-205223

Treatment Outcomes of Patients Treated with Venetoclax-Obinutuzumab Therapy Vs Btki Therapies in 1L CLL: An International Real-World Study

2024· article· en· W4405047353 sur OpenAlexaff
Nicole Lamanna, Jennifer R. Brown, Chaitra S. Ujjani, Toby A. Eyre, Beenish S. Manzoor, Nilanjan Ghosh, Lindsey E. Roeker, Matthew S. Davids, Catherine C. Coombs, Alan P Skarbnik, Brian T. Hill, Hande H. Tuncer, Lori A. Leslie, Joanna Rhodes, Isabelle Fleury, Paul M. Barr, Nnadozie Emechebe, Nicolás Martínez‐Calle, Christopher E. Jensen, Yun Young Choi, Dureshahwar Jawaid, Laurie Pearson, Meghan C. Thompson, Steven E. Marx, Wendy Sinai, Frederick Lansigan, Bita Fakhri, Deborah M. Stephens, Stephen J. Schuster, Michael Coyle, Irina Pivneva, Talissa Watson, Annie Guerin, Mazyar Shadman

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensGroup for Research in Decision AnalysisUniversité de MontréalHôpital Maisonneuve-Rosemont
Organismes subventionnairesnon disponible
Mots-clésVenetoclaxObinutuzumabMedicineInternal medicineOncologyIntensive care medicineChronic lymphocytic leukemiaLeukemia

Résumé

récupéré en direct d'OpenAlex

Introduction: Venetoclax + obinutuzumab (VO) and Bruton tyrosine kinase inhibitor (BTKi) therapies used in the first line (1L) setting are highly effective for patients (pts) with chronic/small lymphocytic leukemia (CLL/SLL) but there are no reported comparative studies. This study compared real-world clinical outcomes of pts treated with VO vs BTKi therapy in 1L. Methods: Data from the CLL Collaborative Study of Real-World Evidence (CORE), an international, retrospective, multi-center chart review study (25 centers) were used. Adult pts who initiated approved 1L treatments of VO (VO cohort) or covalent BTKi (cBTKi cohort) therapy in 1L between 2019-2024 were included. Cohorts were balanced using entropy balancing on sex, age at 1L, year of 1L start, ECOG performance status, Rai stage, del(17p)/TP53 mutation, and comorbidities. Among pts with known mutation status, proportion of pts with unmutated IGHV was similar across cohorts; hence it was not included (VO: 54.8%; cBTKi: 57.6%). Weighted outcomes included overall response rate (ORR: proportion of pts with physician-reported clinical complete/partial response [CR/PR] out of pts with available response data), progression-free-survival (PFS: time from initiation of therapy to disease progression/death [event] or last follow-up [censor]), and time to next treatment or death (TTNT-D: time from initiation of therapy to the change of therapy (including cBTKi to cBTKi)/death [event] or last follow-up [censor]). Time-to-event data were assessed using Kaplan-Meier methods and Cox proportional hazards model at 12 and 18 months (mos). Given >20% of pts were still at risk 18-mos after 1L initiation, results for 18-mos rates were reported. Results: Of 2,309 total pts, 110 initiated VO and 242 a cBTKi (ibrutinib: 111 [45.9%], acalabrutinib: 102 [42.1%], zanubrutinib: 15 [6.2%], cBTKi+anti-CD20: 14 [5.8%]) in 1L. Before weighting, the VO cohort was younger at 1L initiation (median years VO: 64.5; cBTKi: 67.9) and median time to 1L start post-diagnosis was longer (39.1 vs 25.1 mos). The VO cohort had higher proportion of pts with ECOG 0-2 (among those with known ECOG: 99.0% vs 98.9%), lower proportion with del(17p)/TP53 mutation (6.7% vs 17.1%), and lower median number of comorbidities (1 vs 2). Common comorbidities for both cohorts included cardiovascular (43.6% vs 52.1%) and endocrine/metabolic conditions (20.9% vs 36.8%). Pt and treatment characteristics were well-matched between cohorts after weighting. After weighting, with a median follow-up of 10.2 mos (IQR: 5.6, 17.9) for the VO cohort and 10.7 mos (IQR: 4.9, 25.5) for the cBTKi cohort, median duration of treatment was 9.5 [IQR: 5.4, 17.3] vs 12.1 [IQR: 4.9, 18.3] mos, respectively; majority of pts were still on therapy at the time of analyses (63.6% vs 74.7%) but fewer pts in the VO cohort initiated a subsequent line of therapy (2.7% vs 18.8%). Among the 58 pts in the cBTKi cohort who sequenced to another therapy, largely due to intolerance (66.7%), 41.9% switched to another cBTKi (36.2% to acalabrutinib) and 58.1% to a different regimen (i.e., venetoclax-based: 45.7%; other: 12.4%). Only 3 pts in the VO cohort started another therapy (2 due to intolerance). The ORR was descriptively higher for the VO cohort (ORR: 89.2% [CR: 58.1%; PR: 31.1%], available response data for 67.3%) relative to the cBTKi cohort (ORR: 80.7% [CR: 8.8%; PR: 71.9%], available response data for 63.0%). The median PFS was not reached, however the 18-mos rate was trending higher for the VO cohort (92.2% vs 84.3%). Based on the Cox model, there was no statistically significant difference in PFS at 18-mos for the cBTKi cohort compared to the VO cohort (hazard ratio [HR]: 2.37 (confidence interval [CI]: 0.76, 7.44; p-value: 0.14). The median TTNT-D was not reached, however the 18-mos rate was higher for the VO cohort (89.4% vs 70.2%). Based on the Cox model, the cBTKi cohort had a statistically significant greater risk of sequencing to the next treatment/death at 18-mos compared to the VO-based cohort (HR: 3.27 (CI: 1.35, 7.94; p-value: <0.01). Conclusions: This study is one of the first to demonstrate advantages in clinical outcomes of pts using VO vs cBTKi therapy in 1L for TTNT-D. Considering that 19% of BTKi pts switched therapy, largely due to intolerance, this highlights the need for future studies to assess VO vs 2nd-generation BTKis in the 1L setting with longer follow-up time and larger cohorts.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,078
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

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

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 Opus0,029
Tête enseignante GPT0,342
Écart entre enseignants0,313 · 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 tête enseignante, 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
Publié2024
Routes d'admission1
Résumé présentoui

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