Trial in Progress: Investigating the Prognostic Significance of Malnutrition and Sarcopenia in Older Adults with Acute Myeloid Leukemia
Notice bibliographique
Résumé
Background and Significance: For older adults (≥60 years) newly diagnosed with acute myeloid leukemia (AML), tumor-specific and patient-specific factors have been used to predict treatment-related mortality (TRM) and determine suitability for intensive antileukemic therapy. Comprehensive Geriatric Assessment (CGA) is a tool designed to comprehensively evaluate health among older adults. CGA assesses multiple health domains including physical function, cognition, nutrition, mental health, polypharmacy, comorbidities, and social support but does not currently incorporate body imaging. Sarcopenia is defined as loss of muscle mass and strength. It can be measured objectively by CT, DEXA or Bioimpedance Analysis (BIA) in combination with tests of muscle strength. It has been proposed as a more accurate nutritional marker compared to BMI or serum markers. Nearly all older adults with AML receive a CT scan during their cancer evaluation. Therefore, there is an opportunity to leverage CT sarcopenia measures to improve risk prediction. Our study aims to (1) assess the burden of malnutrition and sarcopenia in older adults with newly diagnosed AML undergoing induction and (2) determine the prognostic impact of traditional markers of nutrition and novel sarcopenia measures on TRM. Study Design and Methods: This is a prospective, observational study of 82 newly diagnosed, older adult patients with AML undergoing induction treatment at the University of Chicago Comprehensive Cancer Center (NCT05458258). Key inclusion criteria include age ≥60 years and receipt of induction therapy for newly diagnosed AML. Key exclusion criteria include presence of a pacemaker or defibrillator. To assess Aim 1, newly diagnosed older adult patients with AML will be undergo a subjective global nutrition assessment and serum nutritional markers [prealbumin, albumin, CRP, ferritin]. Body composition (fat-mass, fat-free mass, lean mass) will be assessed using BIA and CT. Sarcopenia will be defined by the CT Skeletal Muscle Index (SMI) at L3 plus impairment on either maximal hand grip strength, 6-minute walk, Timed Up and Go test (TUG), the Short Physical Performance Battery (SPPB). Patients will also undergo an assessment of disability (instrumental activities of daily living (IADL) and activities of daily living (ADL) surveys), Short Physical Performance Battery [SPPB], and Montreal cognitive assessment [MOCA]) prior to starting induction. All measures will be repeated at the start of post-remission therapy. Results from both timepoints will be compared against healthy controls matched by age, sex and Charlson Comorbidity Index. Healthy control data will come from the Frailty, Activity, Body Composition, and Energy Expenditure (FACE) Aging dataset housed by the Department of Geriatrics at the University of Chicago, a 1-year longitudinal, observation study of frailty in older adults residing around the university. We will match 1 AML case to 1 control. In order to ensure that all subjects are matched, 2:1 propensity score matching will be used to generate matching controls for each case. To assess Aim 2, multivariable Cox proportional hazards regression models will be performed for each of those nutrition status and sarcopenia markers significant for TRM in univariate analysis. In multivariable analyses, we will control for age, European Leukemia Net 2022 risk stratification, ECOG PS, and CGA measures as covariates. A sample size of 82 patients will give us 80% power to detect a hazard ratio of 3.0 for TRM, the primary end point, for CT diagnosed sarcopenia using a Cox proportional hazards model with a 0.05 significance level and a 60-day mortality rate of 18%. We anticipate completion of enrollment within two years of study initiation. To date, 17 patients have been approached for consent with 11 patients enrolled. When complete, this trial will provide initial evidence necessary to recommend nutritional assessment as a part of the Comprehensive Geriatric Assessment in all older adult AML patients. Furthermore, it may provide evidence to support a future interventional study assessing the impact of improved nutritional status on TRM. Figure 1. NCT05458258 study schema.
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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».