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Comparison of Adherence Between Nilotinib and Dasatinib as Second-Line Therapies In Chronic Myeloid Leukemia.

2010· article· en· W4256191981 on OpenAlexaff
Annie Guérin, Vamsi Bollu, Amy Guo, James D. Griffin, Andrew P. Yu, Eric Q. Wu

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsNilotinibDasatinibMedicineMyeloid leukemiaImatinibInternal medicineTyrosine-kinase inhibitorMedical prescriptionImatinib mesylateTyrosine kinaseOncologyPharmacologyCancer

Abstract

fetched live from OpenAlex

Abstract Abstract 3437 Background: The recommended treatment option for patients diagnosed with chronic myeloid leukemia (CML) who do not achieve hematological or cytogenetic response to first-line imatinib therapy is to switch to one of the two new BCR-ABL tyrosine kinase inhibitors, nilotinib or dasatinib. Both drugs appear to be efficacious; however, there are no direct comparisons for treatment adherence between nilotinib and dasatinib. Analysis of adherence may reveal whether these two drugs are being used as intended and how adherence issues may potentially affect clinical outcomes. Previously, higher imatinib adherence was associated with significantly lower utilization of resources and costs. (Wu EQ, et al. Curr Med Res Opin. 2010;26:61-69.) The objective of this retrospective study was to compare adherence associated with second-line nilotinib versus dasatinib in a real-world setting. Methods: Two administrative claims databases were combined (MarketScan and Ingenix Impact, 01/2002-12/2008) to identify patients diagnosed with CML (ICD-9 code 205.1x) who had received ≥1 prescription for either nilotinib or dasatinib. Patients were required to have continuous enrollment ≥1 month prior to and after the index date. The index date was defined as the first prescription for nilotinib or dasatinib. Patients were followed for up to 6 months from the index date to the earliest of the termination of health care plan enrollment, or end of data availability. Treatment adherence was measured by the proportion of days covered (PDC) and compared using generalized linear models. PDC was calculated as the sum of the days of supply for nilotinib or dasatinib, divided by the number of calendar days in the study period (i.e., up to 6 months after the index date). Unadjusted average PDCs were compared between nilotinib and dasatinib users using Wilcoxon sum-rank tests. Multivariate regressions were controlled for age, gender, CML disease complexity, any adverse event at baseline, CML year of diagnosis, comorbidities, and bone marrow or stem cell transplant at baseline. Medication possession ratios and discontinuation rates, defined as a treatment gap ≥30 days, were also evaluated. Results: A total of 521 patients receiving a second-line TKI (452 dasatinib and 69 nilotinib) were studied. Patients had a mean age of 57 years, and all other characteristics were similar between the 2 cohorts with the exception that patients in the dasatinib cohort had a longer mean follow-up period compared to those in the nilotinib cohort (161.6 days vs 141.9 days; P = 0.0105). Patients in the dasatinib cohort were less adherent to their therapy compared to nilotinib patients. The PDC (mean ± standard deviation) over the study period was 0.79 ± 0.23 for nilotinib patients and 0.69 ± 0.28 for dasatinib patients. After adjustment, dasatinib patients were estimated to have a 0.096 lower PDC value (approximately 13% lower) compared with nilotinib patients (P = 0.0086). A greater proportion of dasatinib users had a PDC <0.8 and PDC <0.9 (Figure). A statistically significant difference in medication possession ratios at 180 days was also observed; 0.75 ± 0.33 for dasatinib and 0.85 ± 0.27 for nilotinib (P = 0.029). Discontinuation rates were similar between the two drugs with an adjusted hazard ratio (HR) of 1.03 (HR >1 indicates that dasatinib users have a greater discontinuation rate than nilotinib users; 95% confidence interval, 0.63–1.69; P = 0.8981). Conclusions: CML patients treated with second-line nilotinib had significantly better adherence during the 6-month study period than patients treated with second-line dasatinib as defined by the PDC. The medication possession ratio was also greater for nilotinib-treated than dasatinib-treated patients; however, discontinuation rates were similar between the two treatment groups. Although adherence may be related to a greater safety profile as adverse events may disrupt treatment, head-to-head comparisons of the drugs as second-line therapy for CML have not been performed. Further research is necessary to understand and define all factors, including safety, involved in affecting treatment adherence for nilotinib and dasatinib patients. Disclosures: Guerin: Analysis Group, Inc.: Employment. Bollu:Novartis: Employment. Guo:Novartis: Employment. Griffin:Novartis: Consultancy, Research Funding. Yu:Analysis Group, Inc.: Employment. Wu:Analysis Group, Inc.: Employment.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.330
Teacher spread0.297 · 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".

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Citations2
Published2010
Admission routes1
Has abstractyes

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