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Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study
BACKGROUND: In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient’s current health status rather than their prognosis, thus leaving a fatal gap of pat...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337428/ https://www.ncbi.nlm.nih.gov/pubmed/37318826 http://dx.doi.org/10.2196/44042 |
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author | Sigle, Manuel Berliner, Leon Richter, Erich van Iersel, Mart Gorgati, Eleonora Hubloue, Ives Bamberg, Maximilian Grasshoff, Christian Rosenberger, Peter Wunderlich, Robert |
author_facet | Sigle, Manuel Berliner, Leon Richter, Erich van Iersel, Mart Gorgati, Eleonora Hubloue, Ives Bamberg, Maximilian Grasshoff, Christian Rosenberger, Peter Wunderlich, Robert |
author_sort | Sigle, Manuel |
collection | PubMed |
description | BACKGROUND: In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient’s current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. OBJECTIVE: The aim of this proof-of-concept study is to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aim to improve the prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods, and the availability of rescue resources. METHODS: We designed a mathematical model that allows dynamic simulation of the time course of a patient’s vital parameters, depending on individual baseline vital signs and injury severity. The 2 variables were integrated using the well-established Revised Trauma Score (RTS) and the New Injury Severity Score (NISS). An artificial patient database of unique patients with trauma (N=82,277) was then generated and used for analysis of the time course modeling and triage classification. Comparative performance analysis of different triage algorithms was performed. In addition, we applied a sophisticated, state-of-the-art clustering method using the Gower distance to visualize patient cohorts at risk for mistriage. RESULTS: The proposed triage algorithm realistically modeled the time course of a patient’s life, depending on injury severity and current vital parameters. Different casualties were ranked by their anticipated time course, reflecting their priority for treatment. Regarding the identification of patients at risk for mistriage, the model outperformed the Simple Triage And Rapid Treatment’s triage algorithm but also exclusive stratification by the RTS or the NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis and underlined the significance of this novel approach to triage. CONCLUSIONS: The findings of this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline, and time course anticipation. The proposed triage-ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster, and emergency medicine, as well as simulation and research. |
format | Online Article Text |
id | pubmed-10337428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-103374282023-07-13 Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study Sigle, Manuel Berliner, Leon Richter, Erich van Iersel, Mart Gorgati, Eleonora Hubloue, Ives Bamberg, Maximilian Grasshoff, Christian Rosenberger, Peter Wunderlich, Robert J Med Internet Res Original Paper BACKGROUND: In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient’s current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. OBJECTIVE: The aim of this proof-of-concept study is to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aim to improve the prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods, and the availability of rescue resources. METHODS: We designed a mathematical model that allows dynamic simulation of the time course of a patient’s vital parameters, depending on individual baseline vital signs and injury severity. The 2 variables were integrated using the well-established Revised Trauma Score (RTS) and the New Injury Severity Score (NISS). An artificial patient database of unique patients with trauma (N=82,277) was then generated and used for analysis of the time course modeling and triage classification. Comparative performance analysis of different triage algorithms was performed. In addition, we applied a sophisticated, state-of-the-art clustering method using the Gower distance to visualize patient cohorts at risk for mistriage. RESULTS: The proposed triage algorithm realistically modeled the time course of a patient’s life, depending on injury severity and current vital parameters. Different casualties were ranked by their anticipated time course, reflecting their priority for treatment. Regarding the identification of patients at risk for mistriage, the model outperformed the Simple Triage And Rapid Treatment’s triage algorithm but also exclusive stratification by the RTS or the NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis and underlined the significance of this novel approach to triage. CONCLUSIONS: The findings of this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline, and time course anticipation. The proposed triage-ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster, and emergency medicine, as well as simulation and research. JMIR Publications 2023-06-15 /pmc/articles/PMC10337428/ /pubmed/37318826 http://dx.doi.org/10.2196/44042 Text en ©Manuel Sigle, Leon Berliner, Erich Richter, Mart van Iersel, Eleonora Gorgati, Ives Hubloue, Maximilian Bamberg, Christian Grasshoff, Peter Rosenberger, Robert Wunderlich. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.06.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Sigle, Manuel Berliner, Leon Richter, Erich van Iersel, Mart Gorgati, Eleonora Hubloue, Ives Bamberg, Maximilian Grasshoff, Christian Rosenberger, Peter Wunderlich, Robert Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title | Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title_full | Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title_fullStr | Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title_full_unstemmed | Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title_short | Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study |
title_sort | development of an anticipatory triage-ranking algorithm using dynamic simulation of the expected time course of patients with trauma: modeling and simulation study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337428/ https://www.ncbi.nlm.nih.gov/pubmed/37318826 http://dx.doi.org/10.2196/44042 |
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