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Updated: Dec 29, 2025

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Accelerating availability of clinically-relevant parameter estimates from thromboelastogram point-of-care device.

Michelle A Pressly1, Robert S Parker, Matthew D Neal

  • 1From the Department of Chemical and Petroleum Engineering (M.A.P., R.S.P., G.C.), Swanson School of Engineering, Department of Critical Care Medicine (R.S.P., M.D.N., J.L.S., G.C.), Department of Surgery (M.D.N., J.L.S.), and Department of Bioengineering (R.S.P., G.C.), Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA.

The Journal of Trauma and Acute Care Surgery
|February 8, 2020
PubMed
Summary

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A new dynamic model accurately predicts coagulopathy in trauma patients using Rapid Thromboelastogram (TEG) data. This approach enables faster identification of potential maximum amplitude abnormalities and transfusion needs.

Area of Science:

  • Trauma care
  • Coagulation modeling
  • Point-of-care diagnostics

Background:

  • Coagulopathy in trauma patients requires timely detection and management.
  • Traditional thromboelastogram (TEG) analysis can be time-consuming.
  • Dynamic modeling offers a novel approach to predict coagulation status.

Purpose of the Study:

  • To develop and validate a dynamic model for predicting coagulopathy in trauma patients using Rapid TEG data.
  • To assess the model's ability to predict key coagulation parameters and transfusion needs.
  • To determine the time efficiency of the dynamic model compared to traditional methods.

Main Methods:

  • A dynamic model was created using patient-specific parameters (platelet count, activation, growth, and lysis rates).

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  • Over 160,000 virtual Rapid TEGs were generated.
  • Real patient data from STAAMP and PAMPer trials (n=293) were analyzed, comparing patient TEGs to the virtual library.
  • Logistic regression predicted maximum amplitude (MA) abnormalities and transfusion needs using model parameters.

    • Main Results:

    • The algorithm accurately predicted abnormal MA values within minutes of Rapid TEG analysis (AUC 0.95 at 3 min, 0.98 at 10 min).
    • Predictions for platelet and packed red blood cell transfusions were achieved significantly faster than traditional TEG parameters.
    • The model could not reliably predict abnormal lysis (LY30) or fresh-frozen plasma transfusion needs.

    Conclusions:

    • This dynamic modeling approach can be integrated into TEG software for rapid estimation of MA abnormalities.
    • It aids in proactively identifying potential blood product needs for trauma patients.
    • Further refinement may be needed for predicting specific lysis parameters and plasma product requirements.