TCCC Decision Support With Machine Learning Prediction of Hemorrhage Risk, Shock Probability.


Journal

Military medicine
ISSN: 1930-613X
Titre abrégé: Mil Med
Pays: England
ID NLM: 2984771R

Informations de publication

Date de publication:
08 11 2023
Historique:
received: 02 12 2022
revised: 17 02 2023
accepted: 01 08 2023
medline: 13 11 2023
pubmed: 10 11 2023
entrez: 10 11 2023
Statut: ppublish

Résumé

Expected future delays in evacuation during near-peer conflicts in remote locales are expected to require extended care including prolonged field care over hours to days. Such delays can increase potential complications, such as insufficient blood flow (shock), bloodstream infection (sepsis), internal bleeding (hemorrhage), and require more complex treatment beyond stabilization. The Trauma Triage Treatment and Training Decision Support (4TDS) system is a real-time decision support system to monitor casualty health and identify such complications. The 4TDS software prototype operates on an Android smart phone or tablet configured for use in the DoD Nett Warrior program. It includes machine learning models to evaluate trends in six vital signs streamed from a sensor placed on a casualty to identify shock probability, internal hemorrhage risk, and need for a massive transfusion. The project team used a mixed methods approach to create and evaluate the system including literature review, rapid prototyping, design requirements review, agile development, an algorithm "silent test," and usability assessments with novice to expert medics from all three services. Both models, shock (showing an accuracy of 0.83) and hemorrhage/massive transfusion protocol, were successfully validated using externally collected data. All usability assessment participants completed refresher training scenarios and were able to accurately assess a simulated casualty's condition using the phone prototype. Mean responses to statements on evaluation criteria [e.g., fit with Tactical Combat Casualty Care (TCCC), ease of use, and decision confidence] fell at five or above on a 7-point scale, indicating strong support. Participatory design ensured 4TDS and machine learning models reflect medic and clinician mental models and work processes and built support among potential users should the system transition to operational use. Validation results can support 4TDS readiness for FDA 510k clearance as a Class II medical device.

Identifiants

pubmed: 37948287
pii: 7388291
doi: 10.1093/milmed/usad298
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

659-665

Informations de copyright

© The Association of Military Surgeons of the United States 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Christopher Nemeth (C)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Adam Amos-Binks (A)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Gregory Rule (G)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Dawn Laufersweiler (D)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Natalie Keeney (N)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Isaac Flint (I)

Applied Research Associates, Inc., Albuquerque, NM 87110, USA.

Yuliya Pinevich (Y)

Mayo Clinic, Rochester, MN 55905, USA.

Vitaly Herasevich (V)

Mayo Clinic, Rochester, MN 55905, USA.

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