4.6 Article

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin

Journal

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 70, Issue 9, Pages 2667-2678

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2023.3261962

Keywords

Drugs; Biological system modeling; Computational modeling; Adaptation models; Coagulation; Blood; Medical treatment; Adaptive control; anticoagulant; in-silico simulation; model-based control; precision medicine

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This study proposes an adaptive closed-loop control algorithm for warfarin therapy management. In silico validation shows that the algorithm significantly improves the effectiveness of warfarin therapy compared to existing medical guidelines and controllers.
Objective: Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drug-effect uncertainties, requesting frequent monitoring and adjustment. Biovariability in drug absorption and action further complicates titration and calls for individualized strategies. In this paper, we propose an adaptive closed-loop control algorithm to assist in warfarin therapy management. Methods: The controller was designed and tested in silico using an established pharmacometrics model of warfarin, which accounts for inter-subject variability. The control algorithm is an adaptive Model Predictive Control (a-MPC) that leverages a simplified patient model, whose parameters are updated with a Bayesian strategy. Performance was quantitatively evaluated in simulations performed on a population of virtual subjects against an algorithm reproducing medical guidelines (MG) and an MPC controller available in the literature (l-MPC). Results: The proposed a-MPC significantly (p < 0.05) lowers rising time (2.8 vs. 4.4 and 11.2 days) and time out of range (3.3 vs. 7.2 and 12.9 days) with respect to both MG and l-MPC, respectively. Adaptivity grants a significantly (p < 0.05) lower number of subjects reaching unsafe INR values compared to when this feature is not present (8.9% vs.15% of subjects presenting an overshoot outside the target range and 0.08% vs. 0.28% of subjects reaching dangerous INR values). Conclusion: The a-MPC algorithm improve warfarin therapy compared to the benchmark therapies. Significance: This in-silico validation proves effectiveness of the a-MPC algorithm for anticoagulant administration, paving the way for clinical testing.

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