Paediatric major incident triage and the use of machine learning techniques to develop an alternative triage tool with improved performance characteristics
Authors: Chernbumroong S. , Vassallo J. , Malik N.S. , Xu Y. , Keene D. , Lyttle MD. , Smith J.E. , Gkoutos G.V. ,   in collaboration with PERUKI (Paediatric Emergency Research in the UK and Ireland)
Published: December 2021

BackgroundTriage is a key principle in the effective management of major incidents. However, there is an increasing body of evidence demonstrating that existing paediatric methods are associated with high rates of under-triage and are not fit for purpose. The aim of this study was to derive a novel paediatric triage tool using machine learning (ML) techniques.MethodsThe United Kingdom Trauma Audit Research Network (TARN) database was interrogated for all paediatric patients aged under 16 years for the ten-year period 2008-2017. Patients were categorised as Priority One if they received one or more life-saving interventions from a previously defined list. Six ML algorithms were investigated for identifying patients as Priority One. Subsequently, the best performing model was chosen for further development using a risk score approach and clinically relevant modifications in order to derive a novel triage tool (LASSO M2).Using patients with complete pre-hospital physiological data, a comparative analysis was then performed comparing this to existing pre-hospital paediatric major incident triage tools. Performance was evaluated using sensitivity, specificity, under-triage (1-sensitivity) and over-triage (1-positive predictive value).ResultsComplete physiological data were available for 4962 patients. The LASSO M2 model demonstrated the best performance at identifying paediatric patients in need of life-saving intervention, sensitivity 88.8% (95% CI 85.5, 91.5) and was associated with the lowest rate of under-triage, 11.2% (8.5, 14.5). In contrast, the Paediatric Triage Tape and JumpSTART both had poor sensitivity when identifying those requiring life-saving intervention (36.1% (31.8, 40.7) and 44.7% (40.2, 49.4)) respectively.ConclusionThe ML derived triage tool (LASSO M2) outperforms existing methods of paediatric major incident triage at identifying patients in need of life-saving intervention. Prior to its recommendation for clinical use, further work is required to externally validate its performance and undertake a feasibility assessment in a clinical context.What is known about this topicChildren are frequently involved in all types of major incidents. A key principle in their management is triage, the process of prioritising patients on the basis of their clinical acuity.Unlike in the adult population, there are currently only a limited number of paediatric triage tools for use in a major incident, with a paucity of evidence supporting their use.A recent comparative analysis demonstrated that the adult triage tool, the MPTT-24, outperformed all existing pre-hospital paediatric triage tools at determining the Priority One paediatric patient.What this study addsWe have applied machine learning algorithms to derive a novel triage tool, the LASSO M2.This triage tool demonstrated an absolute increase in sensitivity of 52·7% over the existing UK method of pre-hospital paediatric major incident triage, the Paediatric Triage Tape.This study has demonstrated that utilising additional parameters out with patient physiology, can lead to a marked improvement in triage tool performance.