Real-world patient management practices in responders to venetoclax for newly diagnosed acute myeloid leukemia.
Notice bibliographique
Résumé
6527 Background: Venetoclax (VEN) is approved for adult patients (pts) with newly diagnosed (ND) acute myeloid leukemia (AML) in combination with hypomethylating agents (HMAs) or low dose cytarabine. This abstract describes real-world pt management practices among pts with ND AML who respond to VEN+HMA. Methods: The AML Real world evidenCe (ARC) Initiative is a multicenter chart review study of adults with ND AML treated with VEN at 17 academic sites in the US, Israel, and Canada. Pts ineligible for intensive chemotherapy (IC; ie, aged ≥75 years or ≥1 Ferrara criteria comorbidity) who initiated VEN+HMA on or after April 2016 were included (ie, before and after release of product label). Pt management practices in first-line VEN treatment and related impact on duration of response (DoR; assessed with Kaplan-Meier analyses) were examined among pts achieving composite complete remission (CRc; ie, CR or CR with partial hematologic recovery or incomplete count recovery). Results: Among IC-ineligible VEN-treated pts, 116 (60.4%) achieved CRc (median age 73.0 years, 37.9% female, 53.4% European LeukemiaNet 2017 adverse risk, 24.2% Eastern Cooperative Oncology Group grade ≥2). Median DoR was 11.0 months (95% confidence interval: 8.8; 15.2). Most pts (75.9%) received VEN + azacitidine. Median observed VEN treatment duration was 5.8 months and 31.9% remained on VEN as of data entry; 12.1% received hematopoietic stem cell transplant post-VEN. Almost all pts (93.6%) had ≥1 marrow assessment post-VEN initiation, usually in cycle 1 (68.0%) or 2 (19.4%). During VEN treatment, 44.8% received granulocyte colony stimulating factor. Antifungals were used in cycle 1 by 68.1% (83.5% prophylactic; 63.3% strong CYP3A4 inhibitor); DoR did not differ by antifungal use. Most pts (68.1%) had VEN dose ramp-up, from a median of 100 mg to 400 mg daily over 3 days. In cycle 1, 59.5% started with 28 VEN dosing days; this proportion declined in subsequent cycles. Among pts still treated, 48.6% and 54.8% had ≤21 dosing days in cycles 2 and 3, respectively. Most pts achieved CRc in cycle 1 (58.6%) or 2 (21.6%); median DoR did not differ significantly between these pts vs later responders. Among 93 pts treated for ≥1 cycle post-response, most (87.1%) had a dose hold before initiating the next cycle; 51.6% of these 93 pts had a dose hold up to 14 days. Of 50 pts remaining on 28 dosing days until CRc, 26.0% reduced to ≤21 dosing days in the next cycle. Neither postremission dosing days modifications nor between-cycle dose holds significantly impacted DoR. Conclusions: Among VEN-treated ND pts with AML achieving CRc in real-world academic settings, most achieved CRc by the end of cycle 2, consistent with clinical trial results. Nevertheless, timing of response did not appear to affect DoR. Postremission dosing days modifications and between-cycle dose holds were common in clinical practice and did not appear to impact DoR.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».