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Enregistrement W4389246668 · doi:10.1182/blood-2023-173283

Comparative Analysis of Clinical Outcomes and Healthcare Resource Utilization (HRU) in Patients (Pts) with Newly Diagnosed (ND) Acute Myeloid Leukemia (AML) Unfit for High-Intensity Chemotherapy Treated with Venetoclax (VEN) Vs Other Therapies: Results from the AML Real World Evidence (ARC) Initiative

2023· article· en· W4389246668 sur OpenAlexaff
Pinkal Desai, Jacqueline S. Garcia, Joshua F. Zeidner, Sameem Abedin, Ofir Wolach, Pankit Vachhani, Yakir Moshe, Daniel A. Pollyea, Catherine Lai, Thomas W. LeBlanc, Marin Xavier, Boaz Nachmias, Evan C. Chen, Srilakshmi Bathini, Inderpreet Bhatti, Cat N. Bui, Annie Guérin, Rebecca Burne, Esprit Ma, Moshe Grunspan, Neta Frankel, Aaron D. Goldberg

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesnon disponible
Mots-clésMedicineVenInternal medicineChemotherapy regimenCytarabineSurgeryOncologyLeukemiaChemotherapy

Résumé

récupéré en direct d'OpenAlex

Introduction: The BCL-2 inhibitor VEN is approved in combination with hypomethylating agents or low-dose cytarabine for adults with ND AML. The ARC Initiative is a multicenter chart review study of adult pts with AML. This abstract presents real-world clinical outcomes and hospitalizations among ND AML VEN treated pts ineligible for intensive chemotherapy (IC) and their matched controls. Methods: This multicenter chart review study included adult pts (≥18 years) with ND AML who received VEN in combination with HMA on or after April 2016, or non-VEN regimens on or after May 2015, from 15 international academic sites (US: 11; Israel: 4). Pts ineligible for IC (i.e., ≥75 years or ≥1 comorbidity based on the Ferrara criteria) and treated with VEN (VEN cohort) were matched 1:1 to control pts who received non-VEN-based regimens (CON cohort). Pts were matched based on age (<60, 60-74, ≥75 years) and European LeukemiaNet (ELN 2017) risk. All analyses are based on the May 2023 data cutoff; data collection is ongoing. Clinical outcomes include composite complete remission (CRc; i.e., CR, CR with partial hematologic recovery [CRh], or CR with incomplete marrow recovery [CRi]) and overall survival (OS; assessed using Kaplan-Meier analysis). Hospitalizations were assessed overall and among pts who achieved CRc. Results were separately reported overall and among the respective subsets of VEN pts matched to CON pts treated with high-intensity regimens (CON-H) or low-intensity regimens (CON-L). Results: A total of 142 IC-ineligible VEN pts and 142 matched CON pts were included in the analysis, including 80 VEN pts matched to CON-H pts (56.3%) and 62 VEN pts matched to CON-L pts (43.7%) ( Table 1). Overall, VEN pts received VEN-azacitidine (75.4%) or VEN-decitabine (24.6%). A total of 68.3% of all pts were classified as ELN adverse risk, and 24.6% of VEN pts and 20.4% of CON pts were classified as Eastern Cooperative Oncology Group (ECOG) Grade ³2. Among pts with genetic mutations tested (96.5% of VEN pts and 94.4% of CON pts), 22.6% of VEN pts and 14.2% of CON pts had TP53 mutations and 21.9% and 19.4%, respectively, had IDH1/2 mutations. VEN pts had a mean of 11.6 months of follow-up, and CON pts had a mean of 15.0 months of follow-up. VEN pts were significantly more likely to achieve CRc than CON pts (64.7% vs 44.3%, p=0.001). This difference was more pronounced for VEN pts matched to CON-L pts (62.1% vs 18.0%, p<0.001). The 6-month OS rates were not statistically different between VEN pts and matched CON pts (74.7% vs 67.6%; p=0.20), despite the majority of CON pts receiving high-intensity regimens, but improved among VEN pts matched to CON-L pts (73.5% vs 55.3%; p<0.05). VEN pts had fewer days of hospitalization per pt per month (PPPM; mean 6.3 days vs 8.9 days, p=0.045) and shorter mean duration of hospitalization (13.7 days vs 18.2 days, p<0.001) compared with CON pts. A larger difference was seen in the amount of time (days) (6.5 vs 10.5 PPPM, p=0.006) and mean length of hospital stay (13.9 vs 22.4 days, p<0.001) for VEN pts matched to CON-H pts. Among pts with ≥1 all-cause hospitalization, 69.2% of VEN pts and 74.8% of CON pts were hospitalized for treatment administration (p=0.42). Among pts who achieved CRc, VEN pts had fewer days of hospitalization PPPM than CON pts (mean 4.3 days vs 6.8 days, p=0.002). This difference was more pronounced for VEN pts compared to CON-H pts (4.3 days vs 7.7 days, p=0.001). Mean duration of hospitalization was also shorter for VEN vs CON pts (13.6 days vs 20.4 days, p<0.001). Both VEN and CON pts experienced more hospitalizations before CRc was achieved, with statistically fewer hospitalizations for VEN pts compared with CON pts (days of hospitalization PPPM: 10.1 vs 17.4, p=0.001; duration of hospitalization: 16.4 vs 28.2, p<0.001). In the 60 days after CRc was achieved, VEN pts still had fewer days of hospitalization PPPM than CON pts (1.4 vs 5.2, p<0.001). Conclusions: Pts with ND AML who were ineligible for IC and received VEN had significantly higher rates of CRc than matched pts who received non-VEN-based regimens. Although pts treated with VEN-based regimens achieved similar rates of CRc compared to matched control pts treated with high-intensity therapy, pts treated with VEN-based regimens required a significantly lower burden of hospitalizations compared with matched pts on non-VEN-based regimens.

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 enseignants0,005
Score d'incertitude au seuil0,010

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
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
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 Opus0,097
Tête enseignante GPT0,379
Écart entre enseignants0,282 · 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

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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