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Enregistrement W2908606174 · doi:10.1182/blood-2018-99-110223

High Healthcare Utilization and Costs in Patients with Higher-Risk MDS/Low Blast Count AML Treated with Azacitidine in Ontario, Canada

2018· article· en· W2908606174 sur OpenAlexaffabout
Lee Mozessohn, Matthew C. Cheung, Nicole Mittmann, Craig C. Earle, Ning Liu, Rena Buckstein

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentreSunnybrook HospitalUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineAzacitidinePopulationEmergency medicineHealth careEmergency departmentInternal medicineDemographyPediatricsEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Azacitidine (AZA) use in higher-risk MDS has been adopted because it improves survival. Despite this, "real-world" data on the economic impact and resource utilization remains unknown. We used the Ontario provincial AZA MDS registry, which captures all AZA-treated patients in the province, to analyze "real-world" data on healthcare use, associated costs and their predictors in AZA treated higher-risk patients. Methods: We linked the provincial MDS AZA registry (single-payer/universal access), which captures baseline characteristics and treatment response for all AZA-treated patients in Ontario, to population-based health system administrative databases. Only higher-risk MDS patients (IPSS intermediate-2, high) and low blast count AML (21-30% blasts) treated from May 30, 2010 to March 16, 2015 were included. Patients were followed for 24 months following first AZA treatment and censored at the earliest of 90 days after last AZA treatment, date of death, time of acute leukemia induction/allogeneic stem cell transplant or March 31, 2016. We estimated healthcare resource utilization and the mean (and overall) standardized 28-day healthcare cost in Canadian dollars ($1 CDN = 0.76 USD$). Quantile regression was used to explore predictors of cost. Negative binomial regression models were used to explore predictors for higher rate of emergency department (ED) visits, and for longer length of stay, with the natural logarithm of length of follow-up as an offset variable in each model. Results: The registry had 652 higher-risk MDS and 225 low blast count AML patients (n = 877) with median follow up of 8 months (IQR 4-13). Median age was 73 years (IQR 66-79), 66.0% were male, 17.8% were secondary MDS and IPSS scores of those calculable were intermediate-2 (64.9%) and high-risk (35.1%). At the time of AZA initiation, 587 patients (66.9%) were transfusion dependent. The median number of cycles received was 6 (range 3 to 11) and median overall survival was 16.1 months (95% CI 13.9 to 18.3). Overall, 705 patients (80.4%) had at least 1 ED visit and 290 (33.1%) had an ED visit during their first cycle of AZA. In addition, 680 patients (77.5%) had at least 1 hospital admission with a mean hospital stay of 17.7 days (95% CI 16.3 to 19.1) over the entire study period. 141 patients (16.1%) required admission to an intensive care unit. Older age (Rate ratio [RR] = 1.33, 95% CI 1.09-1.62), rurality (RR=1.75, 95% CI 1.42-2.15), high IPSS score (RR=1.31, 95% CI 1.06-1.62), and increased comorbidity level were each independent predictors of increased ED visits; while higher comorbidity level (RR=1.51, 95% CI 1.08-2.11), high IPSS score (RR=1.39, 95% CI 1.01-1.92), and transfusion dependence (RR=1.51, 95% CI 1.13-2.01) were associated with longer hospital stays. The overall mean cost was $146,675 per patient (95% CI $139,537 to $153,812) including AZA and $103,580 (95% CI 98,675 to 108,486) excluding AZA drug costs. The mean standardized cost per 28-day period per patient was $17,638 (95% CI $16, 870 to $18,407) with AZA and $13,450 (95% CI $12,730 to $14,170) without AZA drug costs. Inpatient admissions ($4,631, 95% CI $4,010 to $5,251) and non-physician outpatient cancer clinic costs ($6,092, 95% CI $5,851 to $6,333) were the major cost drivers. Excluding AZA costs, the mean standardized 28-day costs were higher in those receiving less than 4 cycles of AZA (n= 295) at $19,408 (95% CI $17,568 to $21,248), compared with those receiving 4 or more cycles (n= 582) at $10,430 (95% CI $10,069 to $10,790) with inpatient admissions as the major driver (mean $10,192, 95% CI $8,594 to $ 10,192 vs. $1,812, 95% CI $1,558 to $2,065). On multivariable analysis, only greater comorbid disease burden (β = $2,074, 95% CI $665 to $3,483) and transfusion dependence (β = $2,402, 95% CI $1,190 to $3,613) were associated with higher median standardized 28-day cost. Conclusions: In our analysis of "real-world" patients with uniformly higher-risk MDS treated with AZA we demonstrate a significant economic impact above and beyond the cost of AZA alone. The costs are higher in patients who are transfusion dependent and have greater comorbidity and appear to be driven by inpatient care and outpatient non-physician ambulatory care. This group of patients are high users of healthcare resources with the majority having ED visits and inpatient admissions. These results will inform patients and providers about the "real-world" anticipated toxicities of AZA. Disclosures Buckstein: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0,003
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 enseignants0,060
Score d'incertitude au seuil0,434

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,004
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)0,0030,000

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 Opus0,010
Tête enseignante GPT0,227
Écart entre enseignants0,216 · 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

Citations2
Publié2018
Routes d'admission2
Résumé présentoui

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