Major incident triage and the evaluation of the Triage Sort as a secondary triage method

Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools

BACKGROUND

Major incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients-those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have limited evidence supporting their use. This study employs machine learning (ML) to develop and validate models for novel primary and secondary triage tools.

METHODS

Adults (16+ years) from the UK Trauma Audit and Research Network (TARN) registry (January 2008-December 2017) served as surrogates for MI victims, with P1 patients identified using predefined criteria. The TARN database was split chronologically into model training and testing (70:30) datasets. Input variables included physiological parameters, age, mechanism and anatomical location of injury. Random forest, extreme gradient boosted tree, logistic regression and decision tree models were trained to predict P1 status, and compared with existing tools (Battlefield Casualty Drills (BCD) Triage Sieve, CareFlight, Modified Physiological Triage Tool, MPTT-24, MSTART, National Ambulance Resilience Unit Triage Sieve and RAMP). Primary and secondary candidate models were selected; the latter was externally validated on patients from the UK military's Joint Theatre Trauma Registry (JTTR).

RESULTS

Models were internally tested in 57 979 TARN patients. The best existing tool was the BCD Triage Sieve (sensitivity 68.2%, area under the receiver operating curve (AUC) 0.688). Inability to breathe spontaneously, presence of chest injury and mental status were most predictive of P1 status. A decision tree model including these three variables exhibited the best test characteristics (sensitivity 73.0%, AUC 0.782), forming the candidate primary tool. The proposed secondary tool (sensitivity 77.9%, AUC 0.817), applicable via a portable device, includes a fourth variable (injury mechanism). This performed favourably on external validation (sensitivity of 97.6%, AUC 0.778) in 5956 JTTR patients.

CONCLUSION

Novel triage tools developed using ML outperform existing tools in a nationally representative trauma population. The proposed primary tool requires external validation prior to consideration for practical use. The secondary tool demonstrates good external validity and may be used to support decision-making by healthcare workers responding to MIs.

A Scoping Review of Pediatric Mass-Casualty Incident Triage Algorithms

OBJECTIVE

For the pediatric population, there is no consensus on which triage system to use for mass-casualty incidents (MCI). A scoping review was conducted to identify the most accurate triage system for pediatric patients in MCIs.

METHODS

MEDLINE (NLM, Bethesda, MA, USA), Embase (Elsevier Inc., Amsterdam, Netherlands), CINAHL (EBSCO Information Services, Ipswitch, MA, USA), and The Cochrane CENTRAL Register of Controlled Trials (John Wiley & Sons, Hoboken, NJ, USA), as well as Scopus (Elsevier Inc., Amsterdam, Netherlands), Global Health (Centre for Agriculture and Bioscience International, Wallingford, UK), Global Health Archive (Centre for Agriculture and Bioscience International, Wallingford, UK), and Global Index Medicus (World Health Organization, Geneva, Switzerland) were searched for relevant studies that were divided into 3 categories: accuracy of a single system, comparison of 2 or more primary triage system and comparison of secondary triage systems. Grey literature was also searched.

RESULTS

996 studies were identified from which 18 studies were included. Systems studied were found to have poor inter-rater reliability, had a low level of agreement between providers, had missed critically ill patients or were not externally validated. 11 studies compared pediatric MCI triage algorithms using different strategies and the most accurate algorithm was not identified. A recently developed secondary triage system, specifically for pediatric patients, was found to perform better than the comparison triage system.

CONCLUSION

Although some algorithms performed better than others, no primary triage algorithm was accurate enough for the pediatric population. However, only 1 secondary triage algorithm was found to be superior to the others.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence

BACKGROUND

Rapid on-site triage is critical after mass-casualty incidents (MCIs) and other mass injury events. Unmanned aerial vehicles (UAVs) have been used in MCIs to search and rescue wounded individuals, but they mainly depend on the UAV operator's experience. We used UAVs and artificial intelligence (AI) to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.

METHODS

This was a preliminary experimental study. We developed an intelligent triage system based on two AI algorithms, namely OpenPose and YOLO. Volunteers were recruited to simulate the MCI scene and triage, combined with UAV and Fifth Generation (5G) Mobile Communication Technology real-time transmission technique, to achieve triage in the simulated MCI scene.

RESULTS

Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs. Eight volunteers participated in the MCI simulation scenario. The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.

CONCLUSION

The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

New NHS Prehospital Major Incident Triage Tool: from MIMMS to MITT

Triage is a key principle in the effective management of major incidents and is the process by which patients are prioritised on the basis of their clinical acuity. However, work published over the last decade has demonstrated that existing methods of triage perform poorly when trying to identify patients in need of life-saving interventions. As a result, a review of major incident triage was initiated by NHS England with the remit to determine the optimum way in which to triage patients of all ages in a major incident for the UK. This article describes the output from this review, the changes being undertaken to UK major incident triage and the introduction of the new NHS Major Incident Triage Tool from the Spring of 2023.

METASTART: A Systematic Review and Meta-Analysis of the Diagnostic Accuracy of the Simple Triage and Rapid Treatment (START) Algorithm for Disaster Triage

INTRODUCTION

The goal of disaster triage at both the prehospital and in-hospital level is to maximize resources and optimize patient outcomes. Of the disaster-specific triage methods developed to guide health care providers, the Simple Triage and Rapid Treatment (START) algorithm has become the most popular system world-wide. Despite its appeal and global application, the accuracy and effectiveness of the START protocol is not well-known.

OBJECTIVES

The purpose of this meta-analysis was two-fold: (1) to estimate overall accuracy, under-triage, and over-triage of the START method when used by providers across a variety of backgrounds; and (2) to obtain specific accuracy for each of the four START categories: red, yellow, green, and black.

METHODS

A systematic review and meta-analysis was conducted that searched Medline (OVID), Embase (OVID), Global Health (OVID), CINAHL (EBSCO), Compendex (Engineering Village), SCOPUS, ProQuest Dissertations and Theses Global, Cochrane Library, and PROSPERO. The results were expanded by hand searching of journals, reference lists, and the grey literature. The search was executed in March 2020. The review considered the participants, interventions, context, and outcome (PICO) framework and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Accuracy outcomes are presented as means with 95% confidence intervals (CI) as calculated using the binomial method. Pooled meta-analyses of accuracy outcomes using fixed and random effects models were calculated and the heterogeneity was assessed using the Q statistic.

RESULTS

Thirty-two studies were included in the review, most of which utilized a non-randomized study design (84%). Proportion of victims correctly triaged using START ranged from 0.27 to 0.99 with an overall triage accuracy of 0.73 (95% CI, 0.67 to 0.78). Proportion of over-triage was 0.14 (95% CI, 0.11 to 0.17) while the proportion of under-triage was 0.10 (95% CI, 0.072 to 0.14). There was significant heterogeneity of the studies for all outcomes (P < .0001).

CONCLUSION

This meta-analysis suggests that START is not accurate enough to serve as a reliable disaster triage tool. Although the accuracy of START may be similar to other models of disaster triage, development of a more accurate triage method should be urgently pursued.