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Original research
Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting
  1. Max E R Marsden1,2,3,
  2. Zane B Perkins1,3,4,
  3. Erhan Pisirir5,
  4. William Marsh5,
  5. Evangelia Kyrimi5,
  6. Andrea Rossetto1,
  7. Richard L Lyon6,7,
  8. Anne Weaver4,8,
  9. Ross Davenport1,3,
  10. Nigel RM Tai2,3
  1. 1Centre for Trauma Sciences, Queen Mary University of London Blizard Institute, London, UK
  2. 2Academic Department of Military Surgery and Trauma, Defence Medical Services, Lichfield, UK
  3. 3Department of Trauma Surgery, Barts Health NHS Trust, London, UK
  4. 4London’s Air Ambulance, London, UK
  5. 5School of Electronical Engineering and Computer Sciences, Queen Mary University of London, London, UK
  6. 6Air Ambulance Kent, Surrey and Sussex, Redhill, UK
  7. 7School of Health Sciences, University of Surrey, Guildford, UK
  8. 8Emergency Medicine, Barts Health NHS Trust, London, UK
  1. Correspondence to Max E R Marsden; Max.Marsden1{at}nhs.net

Abstract

Background Early intervention in patients with major traumatic injuries is critical. Decision support can improve clinicians’ ability to identify high-risk patients. The aim of this study was to compare the performance of a machine-learning (ML) decision support system to that of expert clinicians and to assess the ML system’s impact on augmenting human risk prediction after injury in the prehospital phase of care.

Methods This early clinical evaluation study compared a ML risk prediction system to expert clinicians in assessing a patient’s risk of trauma-induced coagulopathy (TIC). The study was conducted between 1 January 2019 and 31 June 2019 at two air ambulance sites in the south of England. The ML system used a Bayesian Network algorithm to predict TIC. Comparisons in predictive performance were made first between expert clinicians and the ML system and second, between expert clinicians and expert clinicians exposed to the ML system’s outputs.

Results Overall, 51 expert clinicians were enrolled in the study and 184 patient assessments from 135 patients were analysed. The median age of included patients was 31 years old (IQR 23, 47), 75% were male and median Injury Severity Score 17 (IQR 9, 34). 62 patients (46%) received blood within 4 hours of injury and 26 patients (19%) developed TIC. The ML system did not outperform expert clinicians in discriminating between patients with and without TIC (area under the curve (AUC) ML: 0.87 (95% CI 0.79, 0.95) vs AUC clinician: 0.83 (95% CI 0.74, 0.92), p=0.330)). Calibration and overall accuracy of the ML system were superior. Expert clinicians’ risk prediction, when augmented by the ML system, showed potential for improvement compared with unassisted human performance.

Conclusions Early after injury, an ML system performs well compared with expert prehospital clinicians in the prediction of TIC and blood transfusion. The study suggests that ML systems may augment clinical risk prediction in trauma.

  • Artificial Intelligence
  • trauma
  • pre-hospital care
  • clinical assessment

Data availability statement

Data are available on reasonable request.

https://doi.org/10.1136/emermed-2024-214396

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    Data availability statement

    Data are available on reasonable request.

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    Footnotes

    • Handling editor Shammi L Ramlakhan

    • X @maxmarsden83, @drrichardlyon, @rossdavenport

    • Contributors MERM had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: MERM, ZBP, WM, EK, RLL, RD and NRMT. Aquisition, analysis or interpretation of data: MERM, ZBP, EP, WM, EK, AR, AW, RLL, RD, NRMT. Drafting of the manuscript: MERM, ZBP, NRMT. Critical review of the manuscript for important intellectual content: MERM, ZBP, EP, WM, EK, AR, AW, RLL, RD and NRMT. Statistical analysis: MERM, ZBP, EP, WM, EK and AR.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.