Literature DB >> 29237680

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Kourosh Ravvaz1, John A Weissert2, Christian T Ruff2, Chih-Lin Chi2, Peter J Tonellato2.   

Abstract

BACKGROUND: Clinical trials testing pharmacogenomic-guided warfarin dosing for patients with atrial fibrillation have demonstrated conflicting results. Non-vitamin K antagonist oral anticoagulants are expensive and contraindicated for several conditions. A strategy optimizing anticoagulant selection remains an unmet clinical need. METHODS AND
RESULTS: Characteristics from 14 206 patients with atrial fibrillation were integrated into a validated warfarin clinical trial simulation framework using iterative Bayesian network modeling and a pharmacokinetic-pharmacodynamic model. Individual dose-response for patients was simulated for 5 warfarin protocols-a fixed-dose protocol, a clinically guided protocol, and 3 increasingly complex pharmacogenomic-guided protocols. For each protocol, a complexity score was calculated using the variables predicting warfarin dose and the number of predefined international normalized ratio (INR) thresholds for each adjusted dose. Study outcomes included optimal time in therapeutic range ≥65% and clinical events. A combination of age and genotype identified different optimal protocols for various subpopulations. A fixed-dose protocol provided well-controlled INR only in normal responders ≥65, whereas for normal responders <65 years old, a clinically guided protocol was necessary to achieve well-controlled INR. Sensitive responders ≥65 and <65 and highly sensitive responders ≥65 years old required pharmacogenomic-guided protocols to achieve well-controlled INR. However, highly sensitive responders <65 years old did not achieve well-controlled INR and had higher associated clinical events rates than other subpopulations.
CONCLUSIONS: Under the assumptions of this simulation, patients with atrial fibrillation can be triaged to an optimal warfarin therapy protocol by age and genotype. Clinicians should consider alternative anticoagulation therapy for patients with suboptimal outcomes under any warfarin protocol.
© 2017 American Heart Association, Inc.

Entities:  

Keywords:  anticoagulants; atrial fibrillation; computer simulation; genotype; pharmacogenetics; warfarin

Mesh:

Substances:

Year:  2017        PMID: 29237680      PMCID: PMC5734112          DOI: 10.1161/CIRCGENETICS.117.001804

Source DB:  PubMed          Journal:  Circ Cardiovasc Genet        ISSN: 1942-3268


  35 in total

1.  Integration of genetic, clinical, and INR data to refine warfarin dosing.

Authors:  P Lenzini; M Wadelius; S Kimmel; J L Anderson; A L Jorgensen; M Pirmohamed; M D Caldwell; N Limdi; J K Burmester; M B Dowd; P Angchaisuksiri; A R Bass; J Chen; N Eriksson; A Rane; J D Lindh; J F Carlquist; B D Horne; G Grice; P E Milligan; C Eby; J Shin; H Kim; D Kurnik; C M Stein; G McMillin; R C Pendleton; R L Berg; P Deloukas; B F Gage
Journal:  Clin Pharmacol Ther       Date:  2010-04-07       Impact factor: 6.875

2.  Combined CYP2C9, VKORC1 and CYP4F2 frequencies among racial and ethnic groups.

Authors:  Stuart A Scott; Rame Khasawneh; Inga Peter; Ruth Kornreich; Robert J Desnick
Journal:  Pharmacogenomics       Date:  2010-06       Impact factor: 2.533

3.  A PK-PD model for predicting the impact of age, CYP2C9, and VKORC1 genotype on individualization of warfarin therapy.

Authors:  A-K Hamberg; M-L Dahl; M Barban; M G Scordo; M Wadelius; V Pengo; R Padrini; E N Jonsson
Journal:  Clin Pharmacol Ther       Date:  2007-02-14       Impact factor: 6.875

4.  Secondary stroke prevention with ximelagatran versus warfarin in patients with atrial fibrillation: pooled analysis of SPORTIF III and V clinical trials.

Authors:  Paul T Akins; Harvey A Feldman; Robert G Zoble; David Newman; Stefan G Spitzer; Hans-Christoph Diener; Gregory W Albers
Journal:  Stroke       Date:  2007-01-25       Impact factor: 7.914

5.  PRescriptiOn PattERns of Oral Anticoagulants in Nonvalvular Atrial Fibrillation (PROPER study).

Authors:  Ozcan Basaran; Nesrin Filiz Basaran; Edip Guvenc Cekic; Ibrahim Altun; Volkan Dogan; Gurbet Ozge Mert; Kadir Ugur Mert; Fatih Akin; Mustafa Ozcan Soylu; Kadriye Memic Sancar; Murat Biteker
Journal:  Clin Appl Thromb Hemost       Date:  2015-10-30       Impact factor: 2.389

6.  Association of medication errors with drug classifications, clinical units, and consequence of errors: Are they related?

Authors:  Maki Muroi; Jay J Shen; Alona Angosta
Journal:  Appl Nurs Res       Date:  2016-12-03       Impact factor: 2.257

7.  A pharmacogenetic versus a clinical algorithm for warfarin dosing.

Authors:  Stephen E Kimmel; Benjamin French; Scott E Kasner; Julie A Johnson; Jeffrey L Anderson; Brian F Gage; Yves D Rosenberg; Charles S Eby; Rosemary A Madigan; Robert B McBane; Sherif Z Abdel-Rahman; Scott M Stevens; Steven Yale; Emile R Mohler; Margaret C Fang; Vinay Shah; Richard B Horenstein; Nita A Limdi; James A S Muldowney; Jaspal Gujral; Patrice Delafontaine; Robert J Desnick; Thomas L Ortel; Henny H Billett; Robert C Pendleton; Nancy L Geller; Jonathan L Halperin; Samuel Z Goldhaber; Michael D Caldwell; Robert M Califf; Jonas H Ellenberg
Journal:  N Engl J Med       Date:  2013-11-19       Impact factor: 91.245

8.  Quality of anticoagulation control among patients with atrial fibrillation.

Authors:  Osnat C Melamed; Gilad Horowitz; Asher Elhayany; Shlomo Vinker
Journal:  Am J Manag Care       Date:  2011-03       Impact factor: 2.229

9.  A method to determine the optimal intensity of oral anticoagulant therapy.

Authors:  F R Rosendaal; S C Cannegieter; F J van der Meer; E Briët
Journal:  Thromb Haemost       Date:  1993-03-01       Impact factor: 5.249

10.  Cost-effectiveness of pharmacogenetics-guided warfarin therapy vs. alternative anticoagulation in atrial fibrillation.

Authors:  J Pink; M Pirmohamed; S Lane; D A Hughes
Journal:  Clin Pharmacol Ther       Date:  2013-09-23       Impact factor: 6.875

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