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Record W2980064088 · doi:10.1182/blood.v114.22.169.169

Quantitative Benefit–Risk Assessment of Rivaroxaban for the Prevention of Venous Thromboembolism.

2009· article· en· W2980064088 on OpenAlexaff
Bennett Levitan, Žhong Yuan, Alexander GG Turpie, Richard J. Friedman, Martin Homering, Jesse A. Berlin, Scott D. Berkowitz, Peter M. DiBattiste

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRivaroxabanMedicinePulmonary embolismDeep veinPopulationThrombosisKnee replacementPlaceboInternal medicineSurgeryAbsolute risk reductionWarfarinConfidence intervalArthroplastyAtrial fibrillation

Abstract

fetched live from OpenAlex

Abstract Abstract 169 Introduction: Venous thromboembolism (VTE) is a common complication after hip or knee replacement and is associated with significant morbidity and mortality. Anticoagulants reduce the risk of these complications, but can also result in increased bleeding, thus requiring an assessment of benefit–risk. Patients and Methods: To obtain more precise estimates of treatment effects on low frequency events, data were pooled from 4 phase 3 randomized clinical trials (RECORD1–4) of rivaroxaban vs enoxaparin regimens (or enoxaparin/placebo combination in 1 study) for the prevention of deep vein thrombosis (DVT) and pulmonary embolism (PE) in total hip and knee replacement. Although the studies were designed to answer slightly different questions and differed in treatment duration and comparator dose, pooling was supported by similar study designs, identical endpoints, identical event ascertainment methods, and the same independent central blinded adjudication committees. Benefit–risk was assessed by comparing the excess number of outcome events for benefits vs that for harms (‘risks'), occurring over the treatment period. Excess number of events was defined as the number of events in a hypothetical population of 10,000 patients treated with enoxaparin minus the number of events in such a population treated with rivaroxaban. A positive value indicates that fewer events occur in the population treated with rivaroxaban. The analysis was undertaken for several clinically comparable pairs of composite benefit and harm outcomes: total VTE (any DVT, non-fatal PE, or death from any cause) vs major and clinically relevant non-major (CRNM) bleeding; major VTE (proximal DVT, non-fatal PE, or VTE-related death) vs major bleeding; and symptomatic VTE/all-cause mortality vs major bleeding. For each pair, pooled Mantel–Haenszel weighted risk differences were used to compute the excess number of benefit and harm events, and the difference between excess numbers of events was used to evaluate net clinical benefit (NCB; Table). In all calculations, benefits and risks were weighted equally. An additional assessment was performed using all treatment-emergent serious adverse events (SAEs) as reported by investigators. Results: Rixaroxaban is associated with statistically significantly fewer total VTE, major VTE, and symptomatic VTE/all-cause mortality events than enoxaparin, whereas enoxaparin is associated with a smaller number of different bleeding events, although no bleeding endpoints, other than the composite of major + CRNM bleeding, were statistically significantly different. In each pairwise comparison, the excess number of bleeding events is less than the excess number of VTE-related events by a factor ranging from 4 to 10 (Table). Enoxaparin was also associated with an excess of 194 treatment-emergent SAEs compared with rivaroxaban out of 10,000 patients. In all cases, there is a positive NCB for rivaroxaban vs enoxaparin with 95% confidence intervals (CIs) excluding 0, suggesting that the benefits of rivaroxaban exceed the risks when compared with enoxaparin. Conclusions: This quantitative benefit–risk approach provides a comparison of interventions in clinically relevant population terms. Using the net clinical benefit approach, for a variety of endpoints defining benefits and harms, the benefit–risk profile for rivaroxaban is consistently improved compared with enoxaparin for patients after elective hip and knee replacement. Disclosures: Levitan: Johnson & Johnson Pharmaceutical Research & Development, L.L.C.: Employment, Equity Ownership. Yuan:Johnson & Johnson Pharmaceutical Research & Development, L.L.C.: Employment, Equity Ownership. Turpie:Johnson & Johnson Pharmaceutical Research & Development, L.L.C.: Consultancy; Bayer Schering Pharma AG: Consultancy, Speakers Bureau; sanofi-aventis: Consultancy; GSK: Consultancy; Astellas: Consultancy; Takeda: Consultancy; Portola: Consultancy. Friedman:Boehringer Ingelheim : Consultancy, Research Funding; Johnson & Johnson : Consultancy; Astellas US: Consultancy, Research Funding; Sanofi-Aventis: Consultancy. Homering:Bayer Schering Pharma AG: Employment. Berlin:Johnson & Johnson Pharmaceutical Research & Development, L.L.C.: Employment, Equity Ownership. Berkowitz:Bayer HealthCare Pharmaceuticals: Employment. DiBattiste:Johnson & Johnson Pharmaceutical Research & Development, L.L.C.: 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)0.079
metaresearch head score (Gemma)0.125
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.125
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.025
GPT teacher head0.339
Teacher spread0.314 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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