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Record W3108956418 · doi:10.1093/ehjci/ehaa946.3538

Cost-effectiveness of ticagrelor in patients with type 2 diabetes and coronary artery disease with a history of PCI: an economic evaluation of THEMIS-PCI using a Swedish healthcare perpective

2020· article· en· W3108956418 on OpenAlexaff
Philippe Gabríel Steg, Deepak L. Bhatt, Stephanie James, Oliver Darlington, Louise Hoskin, Tabassome Simon, Kim Fox, Lawrence A. Leiter, Shamir R. Mehta, Robert A. Harrington, Anders Himmelmänn, Wilhelm Ridderstråle, Maria Andersson, Carl Mellström, Phil McEwan

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsMcMaster UniversitySt. Michael's Hospital
FundersAstraZeneca
KeywordsMedicineTicagrelorConventional PCIPercutaneous coronary interventionStroke (engine)PopulationCoronary artery diseaseMyocardial infarctionInternal medicineAspirinCardiology

Abstract

fetched live from OpenAlex

Abstract Background The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS) evaluated ticagrelor compared to placebo for the prevention of myocardial infarction (MI), stroke and cardiovascular (CV) death in 19 220 patients with type 2 diabetes (T2DM) and stable coronary artery disease (CAD) with no prior myocardial infarction (MI) or stroke. THEMIS-PCI was a pre-specified subgroup of 11 154 patients who had a history of percutaneous coronary intervention (PCI) when entering the study. In THEMIS, ticagrelor reduced CV death, MI or stroke, although with an increase in major bleeding compared to aspirin alone, and there was a significant interaction between a prior history of PCI and the net benefit of ticagrelor. In the THEMIS-PCI population, ticagrelor plus aspirin provided a favourable net clinical benefit with a significant 15% reduction in all-cause death, MI, stroke, fatal bleed, or intracranial haemorrhage. Objective The objective of this analysis was to estimate the cost-effectiveness of ticagrelor for the prevention of CV events based on the results of the THEMIS-PCI population using a lifetime horizon from a Swedish healthcare perspective. Methods A lifetime Markov state transition model was developed with health states aligned to the THEMIS trial endpoints. Health state transitions were informed by parametric survival equations fitted to patient level data from THEMIS-PCI population. Treatment discontinuation rates were informed by the THEMIS-PCI population, with all patients assumed to discontinue treatment with ticagrelor after four years. The incidence of bleeding and dyspnoea were modelled as adverse events. Costs (2019 Euros) and utility data were derived from the published literature and the THEMIS-PCI population, respectively, and discounted at 3.0% annually. Probabilistic (PSA) and deterministic sensitivity analysis (DSA) were conducted to quantify uncertainty of key input parameters. Results Treatment with ticagrelor plus aspirin over four years resulted in estimated Quality Adjusted Life Year (QALY) gains of 0.09 at an incremental cost of €1,891 compared to aspirin alone. The estimated incremental cost-effectiveness ratio (ICER) was €19,959/QALY. PSA indicated that ticagrelor was cost-effective in 93% of simulations using a willingness-to-pay threshold of €47,000/QALY and DSA showed that cost-effectiveness was robust to changes in key input parameters (ICER range: €16,504 to €25,012/QALY). Conclusion Based on the results of the THEMIS trial, dual antiplatelet therapy with ticagrelor plus aspirin is likely to be a cost-effective treatment compared with aspirin alone for the prevention of CV events in patients with T2DM and CAD with a history of PCI. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): AstraZeneca

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)0.008
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.298
Teacher spread0.217 · 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 designSimulation or modeling
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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Citations1
Published2020
Admission routes1
Has abstractyes

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