TY  - JOUR
AU  - Sigle, Manuel
AU  - Berliner, Leon
AU  - Richter, Erich
AU  - van Iersel, Mart
AU  - Gorgati, Eleonora
AU  - Hubloue, Ives
AU  - Bamberg, Maximilian
AU  - Grasshoff, Christian
AU  - Rosenberger, Peter
AU  - Wunderlich, Robert
PY  - 2023
DA  - 2023/6/15
TI  - Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study
JO  - J Med Internet Res
SP  - e44042
VL  - 25
KW  - novel triage algorithm
KW  - patient with trauma
KW  - dynamic patient simulation
KW  - mathematic model
KW  - artificial patient database
KW  - semisupervised generation of patients with artificial trauma
KW  - high-dimensional analysis of patient database
KW  - Germany
KW  - algorithm
KW  - trauma
KW  - proof-of-concept
KW  - model
KW  - emergency
KW  - triage
KW  - simulation
KW  - urgency
KW  - urgent
KW  - severity
KW  - rank
KW  - vital sign
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e44042
UR  - https://doi.org/10.2196/44042
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37318826
DO  - 10.2196/44042
ID  - info:doi/10.2196/44042
ER  -