Control-Theoretic Modeling and Prediction of Blood Clot Viscoelasticity in Trauma Patients

Cybermedical systems that regulate patient clotting in real time with personalized blood product delivery will improve treatment outcomes. These systems will harness popular viscoelastic assays of clot strength such as thromboelastography (TEG), which help evaluate coagulation status in numerous conditions: major surgery (e.g., heart, vascular, hip fracture, and trauma); liver cirrhosis and transplants; COVID-19; ICU stays; sepsis; obstetrics; diabetes; and coagulopathies like hemophilia. But these measurements are time-consuming, and thus impractical for urgent care and automated coagulation control. Because protein concentrations in a blood sample can be measured in about five minutes, we develop personalized, phenomenological, quick, control-oriented models that predict TEG curve outputs from input blood protein concentrations, to facilitate treatment decisions based on TEG curves. Here, we accurately predict, experimentally validate, and mechanistically justify curves and parameters for common TEG assays (Functional Fibrinogen, Citrated Native, Platelet Mapping, and Rapid TEG), and verify results with trauma patient clotting data.

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Control-Theoretic Modeling and Prediction of Blood Clot Viscoelasticity in Trauma Patients

Cited by 2 publications

References 28 publications

Control of positive systems with an unknown state-dependent power law input delay and input saturation

Control of positive systems with an unknown state-dependent power law input delay and input saturation

Quick model-based viscoelastic clot strength predictions from blood protein concentrations for cybermedical coagulation control

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