Evidence map›Paper›PMID 32032282›Full record

ArticleThe journal of trauma and acute care surgery2020

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

Michelle A Pressly, Robert S Parker, Matthew D Neal, Jason L Sperry, Gilles Clermont

Abstract read
In one paragraph

Article in The journal of trauma and acute care surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Michelle A PresslyFrom 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.
Robert S Parker
Matthew D Neal
Jason L Sperry
Gilles Clermont

Funding

Mechanistic Elucidation and Targeted Therapy of Platelet Dysfunction After TraumaR35GM119526 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NEAL, MATTHEW D · 2016 to 2025
$3.9M
Endotypes of thrombocytopenia in the critically illR21HL133891 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES, PARKER, ROBERT S. · 2016 to 2017
$398k
NHLBI NIH HHS R21 HL133891NIGMS NIH HHS R35 GM119526
6 · The paper itself

Abstract

backgroundModeling 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).

methodsPatient 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.

resultsThe 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.

conclusionThis 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.

Indexed as

Models, CardiovascularAdultAlgorithmsBlood Coagulation DisordersBlood Component TransfusionClinical Trials as TopicFemaleHumansMaleMiddle AgedPlatelet ActivationPlatelet CountPoint-of-Care SystemsPrognosisThrombelastographyTime Factors

Identifiers

PMID32032282
PMCPMC7887698

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.