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Enregistrement W2992713422 · doi:10.1182/blood.v112.11.4690.4690

Outcomes of Elderly Patients with AML in the Hamilton Area: How Are We Doing?

2008· article· en· W2992713422 sur OpenAlexaff
Sadiya Kukaswadia, Tina Hsu, Parveen Wasi

Notice bibliographique

RevueBlood · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicineMyeloid leukemiaPediatricsPopulationInternal medicineLeukemiaMyelodysplastic syndromesBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Acute myeloid leukemia (AML) is the most common form of leukemia amongst adults. Elderly patients (i.e. over the age of 60) with AML consistently have poorer outcomes than their younger counterparts and very few guidelines exist on the optimal management of AML in this population. A retrospective chart review of patients 60 years of age or older diagnosed with AML was conducted to better define this population, identify biological and patient characteristics that predict outcomes with treatment, and determine factors influencing management decisions. A total of 142 patients diagnosed with AML between April 2002 and April 2007 were included. Patients were analyzed together, as well as in pre-specified age groups (60–65, 66–75, and >75 years). Patients ranged from 60 to 92 years old, with a median of 70 years. Very few patients had a favorable cytogenetic profile (3.5%) at the time of diagnosis (Table 1). In addition, 41.5% of patients had a preceding hematologic diagnosis. Of these, 61.0% had pre-existing myelodysplastic syndrome and 16.9% had chronic myeloid leukemia. The proportion of patients with secondary transformation increased with age (age 60–65 −25%; 65–75 −39%; >75 – 51.3%). Over 60% of patients were induced, with younger patients opting for induction more often (age 60–65 − 88.6%; 65–75 − 66.1%; >75 – 20.5%). The most commonly cited reasons for not treating were the presence of comorbidities (32.1%), patient preference (26.8%), age (17.9%), and preceding hematological conditions (16.1%). Only 25% of those treated were able to complete the entire course of treatment. Despite this, more than half (58.1%) of patients were able to attain remission. As expected, overall survival was dismal across all age groups with 10.6% surviving to one year. Median survival was 2 months with survival decreasing with increasing age (Table 2). With treatment one year survival increased to 15.1% with a median survival of 3.75 months (treated vs. untreated - 15.1% vs. 3.6%, p <0.005). This result was largely driven by survival in the 60–65 year age group, in whom those treated did significantly better than those who were not (1 year survival 20.5% vs. 0%, p <0.005; median 6 vs. 0.18 months). In terms of economic resources, patients who were induced had significantly more outpatient appointments (22.2 vs. 6.4, p <0.0001), hospital days (58.8 vs. 11.6, p <0.0001), and used more blood products (65.7 vs. 12.1, p <0.001), presumably due to increased survival in those who were treated. This dramatic difference between those treated and those who were palliated was seen both in patients age 60–65 and 66–75, but was markedly attenuated in patients older than 75. Our findings are consistent with previous studies. Elderly patients with AML do poorly, with worsening outcomes with increasing age, and survival that is measured in months. This may be due to the increasing prevalence of patients with preceding hematological disorders and secondary transformation with age, as well as poor cytogenetic profiles of this population. In addition, as age and comorbidities increase, more patients opt out of induction chemotherapy. Further research is needed to establish optimal management and improve outcomes of elderly patients with AML. Table 1: Cytogenetics All patients (n; % of patients) Age 60–65 years (n; % of patients) Age 66–75 years (n; % of patients) Age 76 years and greater (n; % of patients) No of Patients 142 44 59 39 Cytogenetics Unknown 40 (28.2%) 10 (22.7%) 11 (18.6%) 19 (48.7%) Favourable 5 (3.5%) 1 (2.3%) 2 (3.5%) 2 (5.1%) 12 Intermediate 59 (41.5%) 23 (52.3%) 24 (40.7%) (30.8%) 6 Unfavourable 38 (26.8%) 10 (22.7%) 22(37.3%) (15.4%) Table 2: Survival Rates All patients (n; % of patients) Age 60–65 years (n; % of patients) Age 66–75 years (n; % of patients) Age 76 years and greater (n; % of patients 1 year survival 15 (10.6%) 8 (18.2%) 5 (8.5%) 2 (5.1%) All 2/56 (3.6%) 0/5 (0%) 1/20 (5.0%) 1/31 (3.2%) Untreated 13/86 (15.1%) 8/39 (20.5%) 4/39 (10.3%) 1/8 (12.5%) Treated (p = 0.004) (p = 0.003) (p = 0.41) (p = 0.23) Median survival (months) 2 3 2 1 All 1 5.5 days 1.5 1 Untreated 3.75 2 6 2.9 Treated Lost to Follow Up 52 (36.3%) 17 (38.6%) 24 (40.7%) 11 (28.2%)

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)0,002
score de la tête « metaresearch » (Gemma)0,015
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 enseignants0,980
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,015
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
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 Opus0,023
Tête enseignante GPT0,256
Écart entre enseignants0,233 · 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

Citations0
Publié2008
Routes d'admission1
Résumé présentoui

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