Evidence map›Paper›PMID 42519522›Full record

ArticleTrauma surgery & acute care open2026

Machine learning for prehospital prediction of trauma-induced coagulopathy: a national evaluation of the potential impact on tranexamic acid administration.

Jared M Wohlgemut, Ateeq Almuwallad, Evangelia Kyrimi, Andrea Rossetto, Max E R Marsden, Erhan Pisirir, Rebecca S Stoner, Elaine Cole, William Marsh, Zane B Perkins and 2 more

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In one paragraph

Article in Trauma surgery & acute care open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jared M WohlgemutCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.ORCID https://orcid.org/0000-0001-8276-0465
Ateeq AlmuwalladCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.
Evangelia KyrimiDepartment of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Andrea RossettoCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.
Max E R MarsdenCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.
Erhan PisirirDepartment of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Rebecca S StonerQueen Mary University of London, London, UK.
Elaine ColeCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.
William MarshDepartment of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0003-0212-6363
Zane B PerkinsQueen Mary University of London, London, UK.
Nigel R M TaiQueen Mary University of London, London, UK.
Ross DavenportCentre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Trauma-induced coagulopathy (TIC) is characterized in part by hyperfibrinolysis. Inhibiting fibrinolysis with tranexamic acid (TXA) decreases mortality in trauma patients, with greater benefit the earlier it is given. However, identifying which patients could benefit has been the subject of controversy. Machine learning models that predict TIC early after injury may aid clinician decision-making. The aim of this study was to identify the proportion of patients with different risks of developing TIC based on TIC-Bayesian network (BN) who received TXA within 1 hour of injury. Methods: This retrospective cohort study included all prospectively-collected adult patients in the UK Trauma Audit Research Network database, between 2015 and 2019, who received hemorrhage control interventions or blood transfusions. Patients were risk-stratified using TIC-BN. Patients were categorized by whether they received TXA ≤1 hour from injury, 1-3 hours from injury, >3 hours from injury, or did not receive it. Results: Of 27,272 included patients, median age was 51 years (IQR 32-71), 68% were male, 86% suffered blunt injury, and median Injury Severity Score was 13 (IQR 9-25). Overall, 5571 (20%) patients received TXA ≤1 hour from injury, including 368/3,403 (11%) with very low risk, 494/4,764 (10%) low risk, 1,805/9,408 (19%) medium risk, 2,003/8,173 (25%) high risk, and 901/1,524 (59%) very high risk of TIC (χ Conclusions: Earlier identification of patients that may benefit from TXA using a machine learning risk prediction model may allow more tailored and prompt treatment for patients at risk of TIC. Level of evidence and type of study: Level III, Retrospective Cohort.

Indexed as

Blood coagulationMultiple Traumatranexamic acidwounds and injuries

Identifiers

PMID42519522
PMCPMC13384119

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