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Enregistrement W2538510330 · doi:10.1182/blood.v114.22.4286.4286

Comparison of Healthcare Utilization and Costs Between Nilotinib and Dasatinib as Second Line Therapies in Chronic Myeloid Leukemia.

2009· article· en· W2538510330 sur OpenAlexaff
Eric Q. Wu, Vamsi Bollu, Amy Guo, Annie Guérin, Magda Tsaneva, Denise Williams, James D. Griffin

Notice bibliographique

RevueBlood · 2009
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesnon disponible
Mots-clésNilotinibMedicineDasatinibInternal medicineMyeloid leukemiaMedical prescriptionPharmacyImatinibPharmacologyFamily medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 4286 Introduction Dasatinib and nilotinib are both indicated to treat chronic myeloid leukemia (CML) patients resistant or intolerant to imatinib. This study compared retrospectively the healthcare resource utilization and costs associated with dasatinib versus nilotinib treatment as second line therapies in CML patients. Patients and Methods Two large administrative claims databases were combined (MarketScan and Ingenix Impact, 01/2002-12/2008) to identify patients diagnosed with CML (ICD-9 code 205.1x) and treated with a tyrosine kinase inhibitor (TKI) second line therapy. Patients with at least 1 prescription of dasatinib or nilotinib and no prior use of TKI other than imatinib were selected to form the dasatinib second line therapy group and nilotinib second line therapy group, respectively. The index date was defined as the first prescription for dasatinib or nilotinib. Only patients with an index date on or after the date of nilotinib FDA approval (10/27/2007) and continuously enrolled at least 1 month prior to and 1 month after the index date were included. Patients were followed for up to 6 months from the index date to the earliest of the termination of healthcare plan enrollment, or end of data availability. Patient total medical visits, as well as outpatient visits and hospitalization days, were compared between the two groups using incidence rate ratios (IRR). Multivariate negative binomial regression models were applied to estimate IRR while adjusting for baseline differences of the two groups. Patient total costs, pharmacy costs, and medical service costs (including costs associated with outpatient visits, inpatient admissions, emergency room visits, and other medical services) were compared between the nilotinib and dasatinib group. Unadjusted and adjusted cost differences were estimated for each cost component using generalized linear models (GLM) or two-part models. Multivariate regression models to compare patient utilization and costs controlled for potential differences in age, gender, and cancer complexity (Darkow 2007) between the two groups. Costs were adjusted for inflation to 2008 U.S. dollars. Results A total of 230 CML patients treated with a second line TKI met the selection criteria; 186 patients treated with dasatinib and 44 patients treated with nilotinib were identified. Average age was similar between the two groups: 56.9 ± 16.3 in dasatinib patients and 54.1 ± 12.4 in nilotinib patients (p=.366) and the ratio of females was not statistically different: 44.1% v 56.8% (p=.128). Comorbidity burden, measured by the Charlson comorbidity index, was also similar between the two groups: 3.12 ± 1.90 for dasatinib patients vs. 3.07 ± 1.95 for nilotinib patients (p=.638), as was the proportion of patients with moderate and severe CML complexity: 53.8% vs 61.4% (p=.362) and 27.4% vs 22.7% (p=.526) for dasatinib and nilotinib treated patients, respectively. Mean duration of prior imatinib treatment for both groups was not statistically significant (p=0.189), 662.1 days vs 583.8 days, for nilotinib Vs dasatinib, respectively. Over the follow-up period, dasatinib patients had significantly more medical visits (IRR=1.32, p=.028), as well as outpatient visits (IRR=1.31, p=.033). Dasatinib patients also had 36% more hospital days but the difference was not statistically significant (IRR=1.36, p=0.664). Over the 6 months following the initiation of the second line therapy, compared to patients on nilotinib, patients on dasatinib incurred $18,328 (p<.001) more in total medical services and $6,367 (p=0.04) less in pharmacy costs, resulting in a higher net total healthcare cost of $12,039 (p=.035). The difference in medical costs was mainly explained by the difference of inpatient costs ($12,480 higher for dasatinib patients; p=<.001) and outpatient costs ($5,035 higher for dasatinib patients; p=.001). Conclusion This preliminary analysis of total cost of treatment data showed that among CML patients treated with a second line TKIs, those treated with dasatinib were associated with higher total healthcare costs and more frequent health care resource utilization than patients treated with nilotinib. Results may be updated when more data on nilotinib patients becomes available. Disclosures: Wu: Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Bollu:Novartis Oncology: Employment. Guo:Novartis Pharmaceuticals Corporation: Employment. Guerin:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Tsaneva:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Williams:Novartis Pharmaceutical Corporation: Employment. Griffin:Novartis Pharmaceutical Corporation: Consultancy, I have.

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)0,005
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 enseignants0,006
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
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 Opus0,050
Tête enseignante GPT0,356
Écart entre enseignants0,307 · 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

Citations1
Publié2009
Routes d'admission1
Résumé présentoui

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