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Influence of model-predicted rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome

Posted on 2019-08-01 - 12:07
Background:

This analysis aimed to evaluate the impact of rivaroxaban exposure and patient characteristics on efficacy and safety outcomes in patients with acute coronary syndrome (ACS) and to determine whether therapeutic drug monitoring might provide additional information regarding rivaroxaban dose, beyond what patient characteristics provide.

Methods:

A post hoc exposure–response analysis was conducted using data from the phase III ATLAS ACS 2 Thrombolysis in Myocardial Infarction (TIMI) 51 study, in which 15,526 randomized ACS patients received rivaroxaban (2.5 mg or 5 mg twice daily) or placebo for a mean of 13 months (maximum follow up: 31 months). A multivariate Cox model was used to correlate individual predicted rivaroxaban exposures and patient characteristics with time-to-event clinical outcomes.

Results:

For the incidence of myocardial infarction (MI), ischemic stroke, or nonhemorrhagic cardiovascular death, hazard ratios (HRs) for steady-state maximum plasma concentration (Cmax) in the 5th and 95th percentiles versus the median were statistically significant but close to 1 for both rivaroxaban doses. For TIMI major bleeding events, a statistically significant association was observed with Cmax [HR, 1.08; 95% CI, 1.06–1.11 (95th percentile versus median, 2.5 mg twice daily)], sex [HR, 0.56; 95% CI, 0.38–0.84 (female versus male)], and previous revascularization [HR, 0.62; 95% CI, 0.44–0.87 (no versus yes)].

Conclusions:

The shallow slopes of the exposure–response relationships and the lack of a clear therapeutic window render it unlikely that therapeutic drug monitoring in patients with ACS would provide additional information regarding rivaroxaban dose beyond that provided by patient characteristics.

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Therapeutic Advances in Cardiovascular Disease

AUTHORS (12)

Liping Zhang
Xiaoyu Yan
Partha Nandy
Stefan Willmann
Keith A. A. Fox
Scott D. Berkowitz
Amarnath Sharma
Anne Hermanowski-Vosatka
Stephan Schmidt
Jeffrey I. Weitz
Dirk Garmann
Gary Peters
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