Nonlinear dynamic modeling and model predictive control of thrombin generation to treat trauma‐induced coagulopathy

Abstract

This article is motivated by the pressing need to robustly automate clinical interventions for trauma-induced coagulopathy (TIC). TIC occurs after severe trauma and shock, and has poor outcomes and about 30% mortality. Although modulating the blood proteins that drive TIC can improve patient outcomes, no practical control-oriented methodology exists to accurately capture biochemical process dynamics and satisfactorily regulate clotting. Hence, we propose a nonlinear dynamic coagulation model that distills the complex biochemical reactions of clotting and also simultaneously generalizes an existing linear phenomenological model. Using our new nonlinear model, we demonstrate the feasibility of model predictive control (MPC) to automate clinical treatments, first in a single-input case that is similar to current open-loop clinical practice, and second in a multi-input case that administers three blood proteins as system inputs to attain satisfactory TIC treatment. The output in both cases is the key clotting protein thrombin. To test robustness, we confirm that both single-input and multi-input MPC are suitable for TIC treatment in the presence of an experimentally observed nonlinearity, an unknown state-dependent power law input delay. Thus, this article provides a strong foundation to transition current open-loop clinical approaches to closed-loop process control.

Dynamic output feedback model predictive control for asynchronous Markovian jump systems under try‐once‐discard protocol

“…Predictive control, a typical digital control strategy, has become an attractive research topic since it emerged originally from the industry in the 1970s, due primarily to its advanced capability of handling multi‐variable control problems with hard constraints 1‐4 . Model predictive control (MPC) based on the state space model, proposed at the end of the last century, is one of the main branches of predictive control, which develops a technical methodology for control synthesis by using mathematical techniques.…”

“…Predictive control, a typical digital control strategy, has become an attractive research topic since it emerged originally from the industry in the 1970s, due primarily to its advanced capability of handling multi-variable control problems with hard constraints. [1][2][3][4] Model predictive control (MPC) based on the state space model, proposed at the end of the last century, is one of the main branches of predictive control, which develops a technical methodology for control synthesis by using mathematical techniques. So far, MPC has gained much focus owing to its great ability, besides the intrinsic optimization control, to obtain the desirable performance under the designed controllers with the help of rigidly technical analysis even for divergent open-loop system.…”