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The Effects of Rivaroxaban Exposure and Clinical Risk Factors on Efficacy and Safety Outcomes in Patients Treated for Venous Thromboembolism

2017· article· en· W3040840390 on OpenAlexaff
Scott D. Berkowitz, Jeffrey I. Weitz, Stephan Schmidt, Keith A.A. Fox, Dirk Garmann, Dagmar Kubitza, Wolfgang Mueck, Gary Peters, Isabel Reinecke, Alexander Solms, Theodore E. Spiro, Xiaoyu Yan, Liping Zhang, Stefan Willmann

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRivaroxabanMedicinePulmonary embolismPopulationCmaxPharmacodynamicsInternal medicineClinical trialRegimenSurgeryAnesthesiaPharmacokineticsWarfarinAtrial fibrillation

Abstract

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Abstract Introduction: The oral factor Xa inhibitor rivaroxaban has previously demonstrated a predictable pharmacokinetic (PK) and pharmacodynamic profile, and has been developed for fixed dose administration without the need for routine coagulation or therapeutic drug monitoring (TDM). The objectives of this study were to investigate the relationship between rivaroxaban exposure and efficacy/safety outcomes, and to evaluate the relative influence of relevant clinical risk factors in patients receiving rivaroxaban for treatment of deep vein thrombosis (DVT) or pulmonary embolism (PE) to assess whether TDM might further improve the benefit-risk profile of rivaroxaban in this population. Methods: An exposure-response analysis was conducted using data from patients receiving rivaroxaban (15 mg twice daily [BID] for 21 days followed by 20 mg once daily [OD] for up to 12 months) in the phase 3 EINSTEIN-DVT (NCT00440193) and EINSTEIN-PE (NCT00439777) trials (BID period, N = 4130; OD period, N = 3953). Due to a lack of measured PK data in the phase 3 trials, individual rivaroxaban exposure metrics (area under the concentration-time curve, maximum concentration [Cmax] and trough concentration [Ctrough]) were estimated using an integrated population PK model based on clinical risk factors, regimen and (where available) PT measurements. Exposure estimates were improved by application of an adjustment function based on a linear relationship between PK and prothrombin time measurements (measured centrally using a rivaroxaban-sensitive thromboplastin reagent) obtained in phase 2/3 rivaroxaban studies. Composite efficacy outcomes were: (1) objectively-documented recurrent DVT, or fatal or non-fatal PE; and (2) recurrent DVT, fatal or non-fatal PE, or death from any cause. Safety outcomes were major bleeding, and a composite of major and non-major clinically relevant (NMCR) bleeding. Relationships between exposure/clinical risk factors and outcomes were evaluated using logistic regression for the 21-day BID dosing period and Cox regression for the subsequent OD dosing period. Results: Of the estimated exposure parameters, Ctrough displayed the strongest associations for efficacy and safety during both dosing periods. The first and second composite efficacy outcomes occurred in 1.0% and 1.1% of patients, respectively, in the BID period, and 1.0% and 1.6% of patients, respectively, in the OD period. Individual predicted Ctrough was significantly associated with the first efficacy outcome in both dosing periods (Table 1), and with recurrent DVT, fatal or non-fatal PE, or death from any cause in the BID period only (Figure 1, Table 1). Patients with reduced creatinine clearance (CrCL; 80 mL/min to develop one of the composite efficacy outcomes during the BID and OD dosing periods (Figure 1, Table 1). Active malignancy at randomization was significant for the second efficacy endpoint in the OD period (hazard ratio 5.31 [95% CI: 2.97-9.51]). Major bleeding and major and NMCR bleeding occurred in 0.4% and 4.3% of patients, respectively, in the BID period and in 0.6% and 6.0% of patients, respectively, in the OD period. None of the investigated exposure metrics was a significant predictor of major bleeding or major and NMCR bleeding in either dosing period. Clinical variables including a history of bleeding, low baseline hemoglobin and non-steroidal anti-inflammatory drug use were, however, significant predictors of bleeding events (Table 2). CrCL was not a significant predictor of bleeding events. Conclusions: Estimated rivaroxaban exposure was significantly associated with efficacy outcomes but not with bleeding outcomes in patients with DVT or PE enrolled in the EINSTEIN-DVT and EINSTEIN-PE trials. Clinical risk factors had a substantial impact on outcomes. Based on these findings, it is unlikely that TDM would further improve the benefit-risk profile of rivaroxaban treatment in patients with venous thromboembolism. Download : Download high-res image (188KB) Download : Download full-size image Disclosures Berkowitz: Bayer US: Employment. Weitz: Daiichi-Sankyo: Consultancy, Honoraria; Novartis Pharmaceuticals: Consultancy, Honoraria; Bayer HealthCare Pharmaceuticals: Consultancy, Honoraria; Janssen Biotech, Inc.: Consultancy, Honoraria; Merck & Co., Inc.: Consultancy, Honoraria; Portola Pharmaceuticals: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Boehringer Ingelheim: Consultancy, Honoraria; Ionis Pharmaceuticals: Consultancy, Honoraria; Pfizer, Inc.: Consultancy, Honoraria. Schmidt: Bayer HealthCare Pharmaceuticals: Consultancy. Fox: AstraZeneca: Research Funding; Janssen Biotech, Inc.: Consultancy, Honoraria; Bayer HealthCare Pharmaceuticals: Consultancy, Honoraria. Garmann: Bayer AG: Employment. Kubitza: Bayer AG: Employment. Mueck: Bayer AG: Employment. Peters: Janssen Research & Development: Employment. Reinecke: Bayer AG: Employment; Bayer AB: Employment. Solms: Bayer AG: Employment. Spiro: Bayer US: Employment. Yan: Janssen Research & Development: Employment. Zhang: Janssen Research & Development: Employment. Willmann: Bayer AG: 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)0.013
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
Metaresearch0.0070.013
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 science0.0000.001
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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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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Published2017
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