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Medical Costs of Adverse Events in Chronic Myeloid Leukemia Patients Treated with Tyrosine Kinase Inhibitors: Canadian Perspective.

2007· article· en· W2564377031 on OpenAlexaffabout
Nick Newton, K. El Ouagari, Mireille Goetghebeur

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsNovartis (Canada)
Fundersnot available
KeywordsNilotinibDasatinibMedicineImatinibMyeloid leukemiaInternal medicineAdverse effectContext (archaeology)Chronic myelogenous leukemiaImatinib mesylateOncologyLeukemia

Abstract

fetched live from OpenAlex

Abstract Background: Imatinib (Gleevec) is recommended first-line therapy for treatment of chronic myeloid leukemia (CML). A relatively small group of patients treated with imatinib develop resistance or are intolerant to the treatment. Dose escalation of imatinib may be used in some cases. Recently, two treatment options, nilotinib (Tasigna) and dasatinib (Sprycel), have become possible alternatives for patients resistant or intolerant to imatinib. Current data indicates that nilotinib and dasatinib have a different side effect profile. Objectives: This study investigated the costs of adverse events (AEs) in patients receiving nilotinib or dasatinib for chronic and accelerated CML. Methods: Incidence rates of grade 3/4 AEs treated with nilotinib or dasatinib were obtained from nilotinib Phase II Summary of Clinical Safety: 120-Day Safety Update Report and dasatinib product information, respectively. Costs for non-hematological AEs were obtained from the Ontario Case Costing Initiative (OCCI) acute inpatient databases, using ICD-10 codes cross-referenced with AEs described in product monographs. For ICD 10 codes identified for this study, there were not enough cases in CML patients (a minimum of five cases is required to access data) and therefore OCCI costs used in this study were those of AEs in oncology patients. These costs were considered a good approximation of costs of AEs in CML patients by the clinical expert. Costs for grade 3 anemia and thrombocytopenia, and non-febrile neutropenia, were assumed to be outpatient costs and were based on literature, expert validation of treatment pathways and resource utilization in the Canadian context. Costs for grade 4 anemia and thrombocytopenia, and febrile neutropenia, were obtained from the OCCI. Multivariate sensitivity analyses were conducted on costs of AEs. The analysis was developed from a payer perspective considering direct medical costs only. Costs are reported in 2006 Canadian dollars. Results: Cost of treatment-related AEs for CML patients was higher for dasatinib than nilotinib. For both treatments, total costs for AEs associated with the accelerated phase were higher than those associated with the chronic phase: $19,902 versus $7,653 for dasatinib; $8,645 versus $3,790 for nilotinib; respectively. Cost attributable to hematological AEs represented between 45% and 71% of total cost of AEs. Ranking observed among treatments for base case costs of AEs was maintained for both high and low cost estimates, indicating that the model was robust to variation in cost of AEs. Conclusions: For patients resistant or intolerant to imatinib, costs of dasatinib-related AEs were approximately twice the costs of nilotinib-related AEs in both chronic and accelerated phases, highlighting the importance of considering the cost of AEs in economic evaluation of new tyrosine kinase inhibitors. Further research is needed to comprehensively evaluate the impact of AEs on healthcare expenditures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.239
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes2
Has abstractyes

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