P519: TRANSFUSION INDEPENDENCE AMONG NEWLY DIAGNOSED ACUTE MYELOID LEUKEMIA PATIENTS RECEIVING VENETOCLAX-BASED COMBINATIONS VS OTHER THERAPIES: RESULTS FROM THE AML REAL WORLD EVIDENCE (ARC) INITIATIVE
Notice bibliographique
Résumé
Background: Venetoclax (VEN), a novel BCL-2 inhibitor, is FDA approved in combination with hypomethylating agents (azacitidine or decitabine) or low-dose cytarabine for the treatment of newly-diagnosed (ND) acute myeloid leukemia (AML) in adults ≥75 years or those who have comorbidities that preclude use of intensive induction chemotherapy. This study is part of the ongoing AML Real world evidenCe (ARC) Initiative which aims to provide insight into the use and relative efficacy of VEN-based combinations among ND patients (pts) with AML. Aims: To describe transfusion independence (TI) among ND AML pts treated with VEN-based combinations vs non-VEN regimens in clinical practice. Methods: The ARC Initiative is a multicenter chart review study of adult pts with AML who received VEN ≥April 2016 (VEN cohort) or non-VEN regimens ≥May 2015 (control cohort). Pts in the VEN cohort were matched 1:1 to pts in the control cohort based on age category (<60; 60-74; ≥75) and European Leukemia Net (ELN) risk classification. Outcomes, including TI, hematopoietic cell transplantation (HCT), and best response, were assessed during first-line therapy over a transfusion observation period defined as time from initiation of therapy to the earliest amongst progression, initiation of second-line therapy, admission to hospice, death, or last visit at study site. TI was defined as no red blood cell (RBC) or platelet (PLT) transfusions during any consecutive ≥56-day period during the transfusion observation period. Kaplan-Meier (KM) analyses were conducted to assess the TI rate at 3 months after treatment initiation. Interim descriptive results are presented from data cutoff of Sept 2021; data will be updated for the meeting. Results: A total of 119 VEN and 119 matched control pts with ND AML who had available information on transfusions were included in this analysis (Table 1). Among pts in the control cohort, 65.5% received a high intensity regimen (e.g., cytarabine+daunorubicin [37 pts, 31.1%], CPX-351 [16 pts, 13.4%]) and 34.5% received a low intensity regimen (decitabine [24 pts, 20.2%], azacitidine [15 pts, 12.6%]). Of pts tested for genetic mutations, common mutations were TP53 (VEN: 23.1%; control: 15.8%), RUNX1 (VEN: 17.1%; control: 17.5%), and IDH1/IDH2 (VEN: 12.8%; control: 14.0%). During a median transfusion observation period of 4.0 months in the VEN cohort and 3.6 months in the control cohort, 87.4% of VEN and 93.3% of control pts received ≥1 RBC or PLT transfusion. Pts in the VEN cohort received a median of 2.0 RBC and/or PLT transfusions per month and 5.0% received HCT; pts in the control cohort received a median of 3.5 RBC and/or PLT transfusions per month and 13.4% received HCT (all p <0.05). Among the 103 pts in the VEN cohort and 99 pts in the control cohort who had ≥56 days of follow-up, 61.2% of VEN pts and 50.5% of control pts achieved TI during first-line therapy (p=0.17), and 60.6% of VEN pts and 55.8% of control pts with response data achieved response (p=0.59). The KM rate of achieving TI at 3 months was 45.0% in the VEN cohort and 35.9% in the control cohort (log-rank p=0.12). Pts in the VEN and control cohort achieved TI after a median of 2.3 and 2.8 months, respectively (p=0.08). Image:Summary/Conclusion: Interim ARC initiative results show that pts receiving VEN-based combinations receive statistically fewer transfusions and are trending towards achieving TI more frequently and reaching TI more quickly after initiation of treatment compared to matched control pts receiving low and high intensity therapies. Further analyses are planned to understand the real-world outcomes of ND pts with AML.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».