Michelle A Pressly1, Robert S Parker, Matthew D Neal, Jason L Sperry, Gilles Clermont. 1. From 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.
Abstract
BACKGROUND: Modeling approaches offer a novel way to detect and predict coagulopathy in trauma patients. A dynamic model, built and tested on thromboelastogram (TEG) data, was used to generate a virtual library of over 160,000 simulated RapidTEGs. The patient-specific parameters are the initial platelet count, platelet activation rate, thrombus growth rate, and lysis rate (P(0), k1, k2, and k3, respectively). METHODS: Patient data from both STAAMP (n = 182 patients) and PAMPer (n = 111 patients) clinical trials were collected. A total of 873 RapidTEGs were analyzed. One hundred sixteen TEGs indicated maximum amplitude (MA) below normal and 466 TEGs indicated lysis percent above normal. Each patient's TEG response was compared against the virtual library of TEGs to determine library trajectories having the least sum-of-squared error versus the patient TEG up to each specified evaluation time ∈ (3, 4, 5, 7.5, 10, 15, 20 minutes). Using 10 nearest-neighbor trajectories, a logistic regression was performed to predict if the patient TEG indicated MA below normal (<50 mm), lysis percent 30 minutes after MA (LY30) greater than 3%, and/or blood transfusion need using the parameters from the dynamic model. RESULTS: The algorithm predicts abnormal MA values using the initial 3 minutes of RapidTEG data with a median area under the curve of 0.95, and improves with more data to 0.98 by 10 minutes. Prediction of future platelet and packed red blood cell transfusion based on parameters at 4 and 5 minutes, respectively, provides equivalent predictions to the traditional TEG parameters in significantly less time. Dynamic model parameters could not predict abnormal LY30 or future fresh-frozen plasma transfusion. CONCLUSION: This analysis could be incorporated into TEG software and workflow to quickly estimate if the MA would be below or above threshold value within the initial minutes following a TEG, along with an estimate of what blood products to have on hand. LEVEL OF EVIDENCE: Therapeutic/Care Management: Level IV.
BACKGROUND: Modeling approaches offer a novel way to detect and predict coagulopathy in traumapatients. A dynamic model, built and tested on thromboelastogram (TEG) data, was used to generate a virtual library of over 160,000 simulated RapidTEGs. The patient-specific parameters are the initial platelet count, platelet activation rate, thrombus growth rate, and lysis rate (P(0), k1, k2, and k3, respectively). METHODS:Patient data from both STAAMP (n = 182 patients) and PAMPer (n = 111 patients) clinical trials were collected. A total of 873 RapidTEGs were analyzed. One hundred sixteen TEGs indicated maximum amplitude (MA) below normal and 466 TEGs indicated lysis percent above normal. Each patient's TEG response was compared against the virtual library of TEGs to determine library trajectories having the least sum-of-squared error versus the patient TEG up to each specified evaluation time ∈ (3, 4, 5, 7.5, 10, 15, 20 minutes). Using 10 nearest-neighbor trajectories, a logistic regression was performed to predict if the patient TEG indicated MA below normal (<50 mm), lysis percent 30 minutes after MA (LY30) greater than 3%, and/or blood transfusion need using the parameters from the dynamic model. RESULTS: The algorithm predicts abnormal MA values using the initial 3 minutes of RapidTEG data with a median area under the curve of 0.95, and improves with more data to 0.98 by 10 minutes. Prediction of future platelet and packed red blood cell transfusion based on parameters at 4 and 5 minutes, respectively, provides equivalent predictions to the traditional TEG parameters in significantly less time. Dynamic model parameters could not predict abnormal LY30 or future fresh-frozen plasma transfusion. CONCLUSION: This analysis could be incorporated into TEG software and workflow to quickly estimate if the MA would be below or above threshold value within the initial minutes following a TEG, along with an estimate of what blood products to have on hand. LEVEL OF EVIDENCE: Therapeutic/Care Management: Level IV.
Authors: Anna Sina P Meyer; Martin A S Meyer; Anne Marie Sørensen; Lars S Rasmussen; Morten B Hansen; John B Holcomb; Bryan A Cotton; Charles E Wade; Sisse R Ostrowski; Pär I Johansson Journal: J Trauma Acute Care Surg Date: 2014-03 Impact factor: 3.313
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Authors: Joshua B Brown; Matthew D Neal; Francis X Guyette; Andrew B Peitzman; Timothy R Billiar; Brian S Zuckerbraun; Jason L Sperry Journal: Prehosp Emerg Care Date: 2014-07-30 Impact factor: 3.077
Authors: James H Lantry; Phillip Mason; Matthew G Logsdon; Connor M Bunch; Ethan E Peck; Ernest E Moore; Hunter B Moore; Matthew D Neal; Scott G Thomas; Rashid Z Khan; Laura Gillespie; Charles Florance; Josh Korzan; Fletcher R Preuss; Dan Mason; Tarek Saleh; Mathew K Marsee; Stefani Vande Lune; Qamarnisa Ayoub; Dietmar Fries; Mark M Walsh Journal: J Clin Med Date: 2022-01-12 Impact factor: 4.241