893 Emergencies in emerging adults: should 16–17-year-olds be cared for in the paediatric or adult ED?

Abstract

Aims Triage 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. Methods The 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 performed with existing prehospital paediatric major incident triage tools. Subsequent external validation was performed using the UK military Joint Theatre Trauma Registry (JTTR). Performance was evaluated using sensitivity, specificity, under-triage (1-sensitivity) and over-triage (1-positive predictive value). Results Complete 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 Jump-START 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. Performance was unchanged in the external validation dataset. Conclusion The ML derived triage tool (LASSO M2) outperforms existing methods of paediatric major incident triage at identifying patients in need of life-saving intervention in both the internal and external validation datasets. Prior to its recommendation for clinical use, further work is required to conduct a feasibility assessment and user acceptability trial in clinical conditions.