Leverages Rapid Blood Protein Measurements to Accurately Predict Clot Strength and Degradation Speed
This blood viscoelasticity assay predicts clot strength from blood protein concentration levels. Trauma is the primary cause of death for people between the ages of 1 and 44, with coagulation and inflammation biology driving most deaths. Clot mechanical properties, like viscoelasticity, are critical to clot function; weak clotting fails to close wounds enough to stop bleeding, while excessive clotting blocks blood vessels entirely and leads to thrombosis, increasing the risk of strokes, heart attacks, and breathing problems.
Exemplary viscoelastic coagulation models use quickly measurable protein concentrations to predict clot strength. Clot strength measurements can identify patients at risk for thromboembolic events and improve patient outcomes in numerous scenarios and conditions such as intensive care unit stays, liver cirrhosis and transplants, cardiac, vascular, and hip fracture surgeries, and trauma responses. Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) are two pervasive and similar viscoelastic coagulation assays that provide information on clot development, stabilization, and dissolution, reflecting in vivo hemostasis. These assays provide patient-specific parameters to clinicians, guiding the delivery of transfusions, blood protein concentrates, pharmacological agents, and anticoagulants. However, each clot strength measurement takes at least an hour to complete; newer, rapid assay versions still take at least half an hour. These time-consuming measurements are impractical for urgent care responses, prevent the automation of interventions, and reduce treatment efficacy. Predictive models outputting TEG and ROTEM assays can offer a faster path to relevant blood clotting parameters, guiding automated and personalized treatment.
Researchers at the University of Florida have developed a rapid viscoelastic, dynamic coagulation assay to accurately predict viscoelastic parameters necessary for determining patient coagulation status. This model uses only blood protein concentration to assess the patient-specific dynamics of clot formation in minutes, replacing time-consuming TEG and ROTEM measurements with model-based computational outputs.
Application
Viscoelastic, dynamic clot strength assay for providing accurate and personalized clot strength predictions
Advantages
- Relies only on blood protein concentration measurements, streamlining the prediction of viscoelastic parameters
- Completes the concentration measurement in 5-10 minutes, providing a significant reduction in clotting information delivery time compared to current TEG tests
- The assay enables implementation in an automated, cyber-medical treatment device, facilitating frequent, personalized, and precise coagulation interventions
- Predicts blood clotting behavior relevant to several disorders, including hemophilia, pulmonary embolism, stroke, cancer, and COVID-19, broadening the scope of potential medical applications Presents potential cost savings to for care centers by eliminating the need for additional equipment and assays (ex: TEG, ROTEM)
Technology
This assay analyzes a patient’s blood to find the concentration of several blood proteins instrumental to clotting. These proteins, known as blood coagulation factors, predict parameters of a personalized phenomenological model that drives the patient’s clotting response. The main component of this response is clot viscoelasticity, governing the mechanical properties of the blood as it transitions from a liquid to a protective clot. The measurement of blood protein concentrations alone captures whole blood viscoelastic properties describing clot formation, stabilization, strength, and degradation. These measurements take only five minutes to predict personalized TEG curve outputs from input blood protein concentrations, hastening treatment decisions that can take hours using TEG machines. This system enables all standard TEG tests, including functional fibrinogen, platelet mapping, RapidTEG, and kaolin. These features provide actionable intelligence for trauma patient care and are implementable in an automated, cyber-medical bedside treatment device.
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