Dataset Information

A Randomized Trial of Pharmacogenetic Warfarin Dosing in Naive Patients with Non-Valvular Atrial Fibrillation.

ABSTRACT:

Unlabelled

Genotype-guided warfarin dosing have been proposed to improve patient’s management. This study is aimed to determine whether a CYP2C9- VKORC1- CYP4F2-based pharmacogenetic algorithm is superior to a standard, clinically adopted, pharmacodynamic method. Two-hundred naïve patients with non-valvular atrial fibrillation were randomized to trial arms and 180 completed the study. No significant differences were found in the number of out-of-range INRs (INR<2.0 or >3.0) (p = 0.79) and in the mean percentage of time spent in the therapeutic range (TTR) after 19 days in the pharmacogenetic (51.9%) and in the control arm (53.2%, p = 0.71). The percentage of time spent at INR>4.0 was significantly lower in the pharmacogenetic (0.7%) than in the control arm (1.8%) (p = 0.02). Genotype-guided warfarin dosing is not superior in overall anticoagulation control when compared to accurate clinical standard of care.

Trial registration

ClinicalTrials.gov NCT01178034.

SUBMITTER: Pengo V

PROVIDER: S-EPMC4692529 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Randomized Trial of Pharmacogenetic Warfarin Dosing in Naïve Patients with Non-Valvular Atrial Fibrillation.

Pengo Vittorio V Zambon Carlo-Federico CF Fogar Paola P Padoan Andrea A Nante Giovanni G Pelloso Michela M Moz Stefania S Frigo Anna Chiara AC Groppa Francesca F Bozzato Dania D Tiso Enrico E Gnatta Elisa E Denas Gentian G Padayattil Jose Seena S Padrini Roberto R Basso Daniela D Plebani Mario M

PloS one 20151228 12

<h4>Unlabelled</h4>Genotype-guided warfarin dosing have been proposed to improve patient’s management. This study is aimed to determine whether a CYP2C9- VKORC1- CYP4F2-based pharmacogenetic algorithm is superior to a standard, clinically adopted, pharmacodynamic method. Two-hundred naïve patients with non-valvular atrial fibrillation were randomized to trial arms and 180 completed the study. No significant differences were found in the number of out-of-range INRs (INR<2.0 or >3.0) (p = 0.79) an ...[more]

PMID: 26710337

Similar Datasets

Project description:BackgroundClinical trials have demonstrated that direct oral anticoagulants (DOACs) are at least non-inferior to warfarin in reducing the risk of stroke/systemic embolism (SE) among patients with non-valvular atrial fibrillation (NVAF), but the comparative risk of major bleeding varies between DOACs and warfarin. Using US Department of Defense (DOD) data, this study compared the risk of stroke/SE and major bleeding for DOACs relative to warfarin.MethodsAdult patients with ≥1 pharmacy claim for apixaban, dabigatran, rivaroxaban, or warfarin from 01 Jan 2013-30 Sep 2015 were selected. Patients were required to have ≥1 medical claim for atrial fibrillation during the 12-month baseline period. Patients with a warfarin or DOAC claim during the 12-month baseline period were excluded. Each DOAC cohort was matched to the warfarin cohort using propensity score matching (PSM). Cox proportional hazards models were conducted to evaluate the risk of stroke/SE and major bleeding of each DOAC vs warfarin.ResultsOf 41,001 identified patients, there were 3691 dabigatran-warfarin, 8226 rivaroxaban-warfarin, and 7607 apixaban-warfarin matched patient pairs. Apixaban was the only DOAC found to be associated with a significantly lower risk of stroke/SE (hazard ratio [HR]: 0.55; 95% confidence interval [CI]: 0.39, 0.77; p < 0.001) and major bleeding (HR: 0.65; 95% CI: 0.53, 0.80; p < 0.001) compared to warfarin. Dabigatran and rivaroxaban initiation were associated with similar risk of stroke/SE (dabigatran: HR: 0.68; 95% CI: 0.43, 1.07; p = 0.096; rivaroxaban: HR: 0.83; 95% CI: 0.64, 1.09; p = 0.187) and major bleeding (dabigatran: HR: 1.05; 95% CI: 0.79, 1.40; p = 0.730; rivaroxaban: HR: 1.07; 95% CI: 0.91, 1.27; p = 0.423) compared to warfarin.ConclusionAmong NVAF patients in the US DOD population, apixaban was associated with significantly lower risk of stroke/SE and major bleeding compared to warfarin. Dabigatran and rivaroxaban were associated with similar risk of stroke/SE and major bleeding compared to warfarin.

Project description:ObjectiveTo compare effectiveness and safety of warfarin and the direct oral anticoagulants (DOAC) dabigatran, rivaroxaban and apixaban in non-valvular atrial fibrillation in routine care.MethodsFrom nationwide registries, we identified treatment-naïve patients initiating warfarin, dabigatran, rivaroxaban or apixaban for non-valvular atrial fibrillation from July 2013 to December 2015 in Norway. We assessed prescription duration using reverse waiting time distribution. Adjusting for confounding in a Cox proportional hazards model, we estimated one-year risks for ischemic stroke, transient ischemic attack (TIA) or systemic embolism, major or clinically relevant non-major bleeding; intracranial; gastrointestinal; and other bleeding. We censored at switch of treatment or 365 days of follow-up.ResultsWe included 30,820 treatment-naïve patients. Compared to warfarin, the adjusted hazard ratios (HR) for ischemic stroke, TIA or systemic embolism were 0.96 (95% CI 0.71-1.28) for dabigatran, 1.12 (95% CI 0.87-1.45) for rivaroxaban and 0.97 (95% CI 0.75-1.26) for apixaban. Corresponding hazard ratios for major or clinically relevant non-major bleeding were 0.73 (95% CI 0.62-0.86) for dabigatran, 0.97 (95% CI 0.84-1.12) for rivaroxaban and 0.71 (95% CI 0.62-0.82) for apixaban. Statistically significant differences of other safety outcomes compared to warfarin were fewer intracranial bleedings with dabigatran (HR 0.28, 95% CI 0.14-0.56), rivaroxaban (HR 0.40, 95% CI 0.23-0.69) and apixaban (HR 0.56, 95% CI 0.34-0.92); fewer gastrointestinal bleedings with apixaban (HR 0.70, 95% CI 0.52-0.93); and fewer other bleedings with dabigatran (HR 0.67, 95% CI 0.55-0.81) and apixaban (HR 0.70, 95% CI 0.59-0.83).ConclusionAfter 1 year follow-up in treatment-naïve patients initiating oral anticoagulation for non-valvular atrial fibrillation, all DOACs were similarly effective as warfarin in prevention of ischemic stroke, TIA or systemic embolism. Safety from bleedings was similar or better, including fewer intracranial bleedings with all direct oral anticoagulants, fewer gastrointestinal bleedings with apixaban and fewer other bleedings with dabigatran and apixaban.

Project description:While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.