Presence of Comorbidities Do Not Predict Early Mortality or Survival in Older Patients (≥60 Years) with Acute Myeloid Leukemia (AML) Undergoing Intensive Induction Therapy.
Notice bibliographique
Résumé
Abstract Older patients with AML undergoing intensive therapy are at a substantial risk of early mortality. These patients frequently have accompanying comorbid conditions, which may contribute towards mortality. The impact of comorbidities on early mortality or survival in older patients with AML is not known. Using the Charlson Comorbidity (CCI) and the Adult Comorbidity Evaluation-27 (ACE-27) indices, we evaluated the impact of comorbidities in 291 newly diagnosed patients ≥ 60 years (APL excluded) with AML (median 68 years; range 60–86) treated with intensive therapy at the Princess Margaret Hospital between Jan 1998 and Dec 2005. Cytogenetics risk categories (MRC-UK classification) at diagnosis: good, 4%; intermediate, 61%; adverse, 20%; and suboptimal/not done, 15%. A preceding hematological disorder was present in 29% patients and 11% had therapy-related AML. ECOG performance status was: 0, 10%; 1, 72%; 2, 16%; and 3, 2%. Of the study patients, 264 (91%) received uniform induction therapy with daunorubicin and cytarabine as described previously (Gupta et al., Cancer, 2005:2082). Median follow-up of survivors was 18 months. ACE-27 was more sensitive in picking up mild comorbidities compared to CCI (121 vs. 64, p<0.0001). Moderate to severe comorbidities according to CCI and ACE-27 were found in 68 (23%) and 88 (30%) patients, respectively. With induction therapy, 159 (54%) patients achieved CR. Early mortality, defined as death due to any cause within 8 weeks from the start of induction, was 18%. Median survival was 317 days (95% CI 274–368) and the probability of 2-year survival was 21% (95% CI 16–26). Patient, disease, and treatment-related factors associated with early mortality and survival were determined using multivariable Cox proportional hazards regression. Only ECOG performance status was associated with early mortality. Notably, age, cytogenetics and comorbidity indices were not associated with early mortality. Poor risk cytogenetics, (p<0.0001), Hb <92 g/L (p<0.0001), WBC count >30 × 109/l (p<0.0001), ECOG PS of 2/3 (p=0.03) and abnormal AST level (p=0.03) at diagnosis were independent factors for overall survival, while age and comorbidity indices were not. We conclude that approximately one third of older patients undergoing intensive induction therapy have significant comorbidities. Early mortality in these patients is influenced by performance status but not by age or the presence of comorbidities, highlighting the need for further research on frailty resulting from the effects of AML. We validated the findings of our previous study (Gupta et al., Cancer, 2005:2082) in a larger data set, demonstrating that survival of these patients is determined by disease biology, rather than age. Age and the presence of comorbidities should not be used as exclusion criteria in determining the candidacy for intensive therapy in older patients with AML.
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 | 0,000 | 0,003 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».