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Enregistrement W4405043182 · doi:10.1182/blood-2024-209217

FRAIL-HRU-AML: Impact of Frailty Assessment on Health Resource Utilization in Acute Myeloid Leukemia Patients: A Population-Based Study from Ontario, Canada

2024· article· en· W4405043182 sur OpenAlexaffabout
Gopila Gupta, Sho Podolsky, Ning Liu, Matthew C. Cheung, Aniket Bankar

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMyeloid leukemiaMedicineLeukemiaPopulationHematologic NeoplasmsGerontologyIntensive care medicineImmunologyEnvironmental healthCancerInternal medicine

Résumé

récupéré en direct d'OpenAlex

Introduction The management of acute myeloid leukemia (AML) involves significant healthcare resource utilization (HRU) due to frequent and prolonged hospitalizations for chemotherapy and supportive care. Frailty, which encompasses overall fitness beyond just comorbidities, is associated with poorer outcomes in various cancers. Assessing frailty can enhance treatment decision-making in oncology by adding valuable context to disease-specific factors. However, the specific impact of frailty on HRU in AML has not been well studied. Therefore, this study aims to evaluate the impact of frailty on HRU in AML patients. Methods This retrospective cohort study from population-based health administrative databases in Ontario, Canada (ICES) included all patients (pts) ≥18 years newly diagnosed (ND) with AML between 2006 and 2021 and treated within 90 days after diagnosis. Patients were followed from date of first chemotherapy to 1- year after maximum follow up until March 31, 2023, for HRU outcomes. Patients were censored at the time of allogenic stem cell transplant (ASCT). The primary predictor, frailty was measured using McIsaac's frailty index (MFI) and categorized as fit (FT), pre-frail (PFR), or frail (FR). HRU outcomes included length of stay for all hospitalizations in days (Total-LOS), intensive care unit stay in days (ICU-LOS), and number of hospital admissions including emergency visits (HA) within first year after starting chemotherapy. These outcomes were measured as per person year (PPY) to adjust for variability in length of follow-up. Association of frailty with HRU outcomes was measured as rate ratios (RR) using multivariable negative binomial models. Model co-variates included age, sex, rurality, neighborhood income quintile, Ontario marginalization (ON-MARG), co- morbidities, ethnicity, prior non-AML malignancy, and treatment intensity {classified as intensive (IT) or non-intensive (NIT) based on standard practices}. Results This study included 5450 pts with ND- AML, with a median age of 65 years (IQR 54-74), 55.8% being males. 3543 (65%) patients received IT and 1907 (35%) received NIT. In entire cohort, 1750 (32.1%) patients were FT, 1874 (34.4%) PFR, and 1826 (33.5%) FR. In 2,035 (37%) patients ≤ 60 years, 44.5% (905) were FT, 36.7% (746) PFR, and 18.9% (384) FR. In 3,415 (63%) patients > 60 years of age, 24.7% (845) patients were FT, 33.0% (1,128) PFR and 42.2% (1,442) FR. 39.0% (683) of FT, 28.7% (537) of PFR, and 18.7% (342) of FR patients underwent ASCT. Median follow up for entire cohort was 13 months (IQR: 4-34). Median overall survival (months) was 12.5 (95% CI: 12.0-13.2) in the entire cohort, 17.6 (95% CI: 16.2-19.1) in FT, 13.7 (95% CI: 12.6-15) in PFR, and 8.5 (95% CI: 7.6-9.3) for FR patients. On univariate analysis, the total LOS (days) was longer for FT patients: 62.04 (95% CI: 61.62-62.46) for FT, 52.29 (95% CI: 51.91-52.68) for PFR, and 55.69 (95% CI: 55.25-56.13) for FR patients, with statistical significance (p<0.0001). However, ICU-LOS (days) was longer (p<0.0001) for FR patients with a median ICU-LOS of 3.15 (95% CI: 3.05-3.26) for FR, 2.41 (95% CI: 2.33-2.49) for PFR, and 2.32 (95% CI: 2.24-2.40) for FT patients. Similarly, HAs were more frequent in FR patients, 5.63 (95% CI: 5.49-5.77) for FR, 4.99 (95% CI: 4.88-5.11) for PFR, and 5.18 (95% CI: 5.06-5.30) for FT patients, showing statistical significance (p<0.0001). On multivariable analysis for total-LOS, frail patients had significantly higher total- LOS (RR-1.17, 95% CI- 1.06-1.29, p=0.0009), compared to, FT patients (ref.). Advanced age, sex, intensity of chemotherapy and presence of secondary AML were not significantly associated with total- LOS. Frailty was also associated with higher ICU- LOS (RR-1.81, 95% CI- 1.35-2.4, p<0.001) compared to fit patients (ref.). Patients older than 65 years also had higher ICU-LOS (p<0.001) and female patients had lower ICU-LOS. Intensity of treatment had no significant association with ICU-LOS. Similarly, FR patients had significantly higher HA (RR-1.12, 95% CI- 1.05-1.20, p=0.0005) than FT patients (ref.). Advanced age above 65 years of age, those receiving IT and patients with secondary AML also showed independent association with increased HA. Conclusion Frailty is independently associated with higher total-LOS, ICU-LOS and HA in ND- AML patients within 1 year of starting chemotherapy after adjusting for advanced age, sex, intensity of chemotherapy and secondary 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 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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,025
Score d'incertitude au seuil0,182

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,005
Études des sciences et des technologies0,0020,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,029
Tête enseignante GPT0,344
Écart entre enseignants0,314 · 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
Publié2024
Routes d'admission2
Résumé présentoui

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