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Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of

Manuel Sigle1,2, Leon Berliner1, Erich Richter3

  • 1University Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Eberhard Karls University, Tübingen, Germany.

Journal of Medical Internet Research
|June 15, 2023
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Summary

This study introduces a novel patient triage model that ranks urgency by anticipated survival time, improving casualty prioritization in mass casualty incidents. The new algorithm outperforms existing methods in identifying patients at risk for mistriage.

Keywords:
Germanyalgorithmartificial patient databasedynamic patient simulationemergencyhigh-dimensional analysis of patient databasemathematic modelmodelnovel triage algorithmpatient with traumaproof-of-conceptranksemisupervised generation of patients with artificial traumaseveritysimulationtraumatriageurgencyurgentvital sign

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Area of Science:

  • Emergency Medicine
  • Medical Simulation
  • Trauma Care

Background:

  • Current triage algorithms focus on immediate status, not prognosis, leading to mistriage in mass casualty incidents.
  • Existing methods fail to account for individual injury patterns and resource availability.
  • A critical gap exists in accurately prioritizing patients based on survival likelihood.

Purpose of the Study:

  • To develop and demonstrate a novel triage approach ranking patients by anticipated survival time without intervention.
  • To improve casualty prioritization by integrating individual injury patterns, vital signs, and resource availability.
  • To create a proof-of-concept model for enhanced emergency medical triage.

Main Methods:

  • Developed a mathematical model for dynamic simulation of patient vital parameters over time.
  • Integrated Revised Trauma Score (RTS) and New Injury Severity Score (NISS) into the model.
  • Generated an artificial patient database (N=82,277) for time course modeling and comparative analysis of triage algorithms.
  • Utilized Gower distance clustering to visualize patient cohorts at risk for mistriage.

Main Results:

  • The proposed algorithm realistically modeled patient survival trajectories based on injury severity and vital parameters.
  • Casualties were ranked by anticipated time course, reflecting treatment priority.
  • The model outperformed existing algorithms (Simple Triage And Rapid Treatment, RTS, NISS) in identifying patients at risk for mistriage.
  • Multidimensional analysis successfully separated patients with similar profiles into distinct risk clusters.

Conclusions:

  • The novel triage-ranking algorithm is feasible and relevant, offering a unique system for prognosis and time course anticipation.
  • This innovative approach has broad applications in prehospital, disaster, and emergency medicine.
  • The model signifies a significant advancement in triage methodology for improved patient outcomes.