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Enregistrement W2753511274 · doi:10.1182/blood.v128.22.4338.4338

Azacitidine Use in the Real World Does Not Replicate AZA-001 Results in Higher Risk MDS/Low Blast Count AML: An Audit of 1101 Patients in the Cancer Care Ontario Registry

2016· article· en· W2753511274 sur OpenAlexaffabout
Lee Mozessohn, Matthew C. Cheung, Saber Fallahpour, Tripat Gill, Asmaa Maloul, Liying Zhang, Olivia Lau, Rena Buckstein

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensHealth Sciences CentreCancer Care OntarioSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineHazard ratioPopulationProportional hazards modelCancer registryCancerConfidence interval

Résumé

récupéré en direct d'OpenAlex

Abstract Background: In the AZA-001 trial, azacitidine (AZA) altered the natural history of patients with higher-risk MDS (Fenaux et al., Lancet 2009) by significantly improving overall survival compared to conventional care regimens (24.5 months versus 15.0 months). Once approved for drug reimbursement by the provincial health ministry, Cancer Care Ontario (CCO) mandated that all eligible patients be enrolled in a prospective registry to ensure compliance with eligibility criteria and schedules of drug administration. Baseline characteristics were recorded and treatment response were to be submitted every 6 cycles of treatment. Our objectives were to audit this clinical program for results after 6 years and identify the prognostic markers for survival in a homogenous higher-risk MDS/low blast count AML patient population. Methods: Only higher-risk MDS patients (intermediate-2, high) as defined by the International Prognostic Scoring System (IPSS) and low blast count AML (20-30% blasts) treated with AZA in Ontario, Canada from June 1, 2010 to March 2, 2016 were eligible and included. Our primary outcome was overall survival (OS) from date of first AZA treatment. Our secondary outcomes were overall response rates and the predictors of OS including administration schedules, centre size or type (regional cancer versus community). Univariate and multivariable Cox proportional hazard model were used to determine the predictors of OS. Hazard ratios and generalized R2 (higher the R2, stronger association with OS) were also calculated. Results: 825 higher-risk MDS and 276 low blast count AML were included (n = 1101 total). Median age was 74 years (range 19 to 99), 65.2% were male and the IPSS scores were intermediate-2 (64.3%) and high-risk (35.7%). Sixty-six percent of patients were transfusion dependent (TD) at time of AZA initiation and 15.5% had received previous chemotherapy. By dosing schedule, 24.7% received AZA for 7 consecutive days (7d), 12.4% for 6 consecutive days (6d) and 62.9% by the 5-2-2 schedule. Overall, the median number of cycles received was 6 (range 1 to 67) and 8 (range 6 to 14) when restricted to the 692 (63%) patients who received at least 4 cycles of treatment. Dose reductions were seen in 33.3% of patients (mean 11.1 mg/m2 in those with reductions) and were more common over time (negative slope 0.178; p < .0001). Of those with repeat bone marrow (n = 293) best response was complete response (CR) in 16.7% and partial response (PR) in 10.6%. Of those without CR/PR/progressive disease on bone marrow (n = 814), 20.4% experienced hematologic improvement including those with marrow CR and marrow stable disease. The actuarial median survival was 11.6 months (95% CI 10.7- 12.4) with a significantly longer OS for MDS compared with low blast count AML (12.4 months vs. 9.6 months; p = .0002) and 16.7 months (95% CI 15.2-18.1) for those receiving at least 4 cycles. There was no difference in OS between the 3 dosing schedules (11.7 months (7d) vs. 10.2 months (6d) vs. 12.0 months (5-2-2); p = .87; figure 1A), regional cancer centre vs. community (11.3 months vs. 11.7 months; p = .19; figure 1B) or by centre volume (11.4 months < 50 patients vs. 11.6 months > 50 patients; p = .38; figure 1C). On univariate analysis, the following were predictive of OS: blast percentage, number of cytopenias, karyotype, IPSS, WHO classification, TD, greater transfusion burden and secondary MDS. The multivariate model with the highest R2 (13.5%) included blast percentage (p < .0001), karyotype (p < .0001), cytopenias (p = .038), TD (p < .0001) and secondary MDS (p = .0006) as summarized in the table below. Conclusions: In our large real world evaluation of AZA use in higher-risk MDS/low blast count AML, we validated the expected overall response rates to AZA but demonstrated a lower than expected OS compared to the AZA-001 trial. Reassuringly, survival did not differ by dosing schedules, centre volumes or center type (regional cancer vs. community). OS was higher in the 2/3 of patients who received at least 4 cycles of treatment, reinforcing the necessity of sustained administration until therapeutic benefits are realized. This represents the largest real world evaluation of AZA in higher-risk MDS/low blast count AML and additional analyses are underway. Table Table. Figure 1 Figure 1. Disclosures Mozessohn: Celgene: Honoraria. Buckstein:Celgene: Honoraria, Research Funding; Novartis: Honoraria.

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)0,003
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 enseignants0,514
Score d'incertitude au seuil0,966

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,004
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
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 Opus0,031
Tête enseignante GPT0,286
Écart entre enseignants0,255 · 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

Citations2
Publié2016
Routes d'admission2
Résumé présentoui

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