PF280 INTENSIVE TREATMENT OF ELDERLY AML PATIENTS IS SAFE AND FEASIBLE, PRINCESS MARGARET CANCER CENTRE EXPERIENCE
Notice bibliographique
Résumé
Background: Acute myeloid leukemia (AML) is a clinically and biologically heterogeneous disease. The incidence of AML increases with age, with the median age at diagnosis currently 67 years. The outcome in older patients is worse than for younger patients, which relates to patient, disease characteristics and possibly changes in drug dosing. Treatment represents a challenge as there is variability in which of these patients will be offered curative intent therapy. Aims: To describe the clinical outcomes in older adults with acute myeloid leukemia treated with induction chemotherapy. Methods: A retrospective review was performed on all patients with a new diagnosis of AML defined by ≥20% blasts in peripheral blood or bone marrow (acute promyelocytic leukemia and myeloid sarcoma excluded), treated intensively at Princess Margaret Cancer Centre between February 2015 and August 2017. Statistical analysis with Kaplan‐Meier product‐limit method, Log‐rank test and Cox regression model were performed using version 9.4 of the SAS system for Windows (2002–2012 SAS Institute, Inc., Cary, NC). Results: We identified 283 patients with a new diagnosis of AML for whom induction chemotherapy was used as the frontline approach. Of these, 136 (48%) were ≥60 years (range 60–82). Demographic and clinical data of both age groups were well balanced. The median age at presentation for the younger cohort was 50 yrs (range 18–59) while for the older group was 68 yrs (range 60–82). Poor risk cytogenetics by MRC classification was more frequently seen in the younger population compared to the older group (27% vs 17%, P = 0.030). Type of induction chemotherapy was evenly distributed between the two groups. In total, 216 patients had a beneficial response (CR/CRi/morphologic leukemia free state) with no differences between the younger and older age groups (76% vs 77%, P = 0.73). There was no significant difference in early death between young (3%) and older population (5%) ( P = 0.339). In the Univariate Cox regression model comparing the two groups, MRC Cytogenetics ( P < 0.0001), type of induction chemotherapy ( P = 0.0079), post induction response ( P < 0.0001), white blood cell count ( P < 0.0001), lactate dehydrogenase ( P = 0.0015) and creatinine ( P = 0.03) were predictive of a shorter survival. Neither the type of diagnosis or ECOG retained prognostic value. In the multivariate analysis only post induction response remained statistically significant for length of survival ( P < 0.0001). With a median follow‐up of 13 months, older patients had a shorter OS (21.0 months) vs 31.6 months in the younger group ( P = 0.0121), Figure 1. The older population was subsequently subclassified into 2 different age groups, 60–69 yrs, 70–79 yrs (1 patient ≥ 80 was identified and excluded from analysis) to further investigate the effect of increasing age on outcomes. Survival for the 60–69 yrs and 70–79 yrs was 22.0 and 17.2 months respectively ( P = 0.473) Figure 2 Summary/Conclusion: This study shows that those older patients undergoing intensive chemotherapy have similar response rates and treatment related mortality compared to their younger colleagues. Furthermore, patients in the 70–79 group had similar median OS compared to their younger counterparts (60–69 yrs). Despite a greater proportion of younger patients having poorer risk disease, the older group had a worse OS suggesting that other disease and patient related factors are important. Transplant‐Molecular data using a 54 myeloid gene panel will also be presented at EHA. image
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».