Key result
An individualized mathematical model of blood coagulation during warfarin treatment demonstrated that blood flow velocity and antithrombin levels should be considered alongside INR to predict therapeutic ranges.
Why the study?
Does an individualized blood coagulation model predict the targeted INR therapeutic range during warfarin treatment in virtual patients?
Does an individualized blood coagulation model predict the targeted INR therapeutic range during warfarin treatment in virtual patients?
An individualized mathematical model of blood coagulation suggests that blood flow velocity and antithrombin levels should be considered alongside INR to optimize warfarin dosing and prevent thrombosis or bleeding.
May refine warfarin monitoring models beyond INR; leaves open prospective outcome validation before practice change.
Deep venous thrombosis (DVT) is characterized by formation of blood clot within a deep vein. The resulting thrombus can partially or completely block blood circulation. It can also detach and migrate with the flow resulting in pulmonary embolism. Anticoagulant drugs such as warfarin are usually prescribed to prevent recurrent thrombosis. The action of warfarin is monitored using a blood test for the International Normalized Ratio (INR) which is based on prothrombin time measurement. A high INR indicates a predisposition of the patient to bleeding, while a low INR shows that the warfarin dose is insufficient to prevent thromboembolic events. The therapeutic target of INR varies from case to case depending on clinical indications. It tends to be in the range 2.0 – 3.0 in most conditions. In this work we develop a model describing blood clotting during warfarin treatment. The action of warfarin is introduced by a Pharmacokinetics-Pharmacodynamics (PK-PD) sub-model. It describes the inhibition of synthesis of the vitamin K dependent factors by warfarin in the liver. We generate a population of patients with individual characteristics and assess their response to warfarin treatment by comparing the simulated INR and the corresponding developed clot height. Using this approach, we determine the underlying causes behind thrombosis and bleeding persistence even for an INR in the normal range. Thus, we suggest a novel methodology to predict the targeted INR depending on individual patient characteristics.
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Bouchnita et al. (2016) studied Deep venous thrombosis risk (n=200). Warfarin (simulated) vs. Different doses was evaluated on INR and percent of vessel occupied by the clot. An individualized mathematical model of blood coagulation during warfarin treatment demonstrated that blood flow velocity and antithrombin levels should be considered alongside INR to predict therapeutic ranges.
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