Abstract 343: Prediction of Lifesaving Interventions Using Changes in Vital Signs from Prehospital Scene to Emergency Department

Abstract

Objective: We investigate whether changes in vital signs between the prehospital scene and emergency department (ED) can be used to develop triage tools to predict the need for life-saving interventions (LSI) and survival in trauma patients. Methods: We analyzed a prospective cohort with any prehospital systolic blood pressure (SBP) ≤ 90 mmHg or Glasgow Coma Scale ≤ 8 who were admitted to an ED at 11 sites of the Resuscitation Outcomes Consortium. The primary outcome was the need for in-hospital LSI (e.g. invasive airway management, invasive bleeding control, blood transfusion, craniotomy, cardiopulmonary resuscitation). Secondary outcome was survival to hospital discharge. Changes in heart rate (HR), SBP, shock index (SI), and respiratory rate (RR) from first prehospital assessment to first ED assessment were considered as predictors in addition to sex, age, mechanism of injury, trauma center level, duration of transport, type of transport, and prehospital fluid volume. Decision trees for each outcome were developed using binary recursive partitioning with predictive performance measured using sensitivity, specificity, and classification error. Results: 5625 subjects were included in our analysis with 49% in need of LSI and 21% dying prior to discharge. Patients needing an LSI tended to either: (1) have an increasing SI (delta ≥ 0.22), (2) have a decreasing SI (delta < 0.22) and >500 mL prehospital fluids, or (3) have a decreasing SI (delta < 0.22), ≤500 mL prehospital fluids, and large change in RR (delta ≥ 9.5 or delta < -7.5). Those surviving to discharge tended to either: (1) have a decreasing SI (delta < 0.57) and a HR that did not decrease greatly (delta > -47) or (2) have an increase in SI (0.57 ≤ delta < 1) and a declining RR (delta < 5). LSI tree had a sensitivity of 58.7% and specificity of 63.3%. Survival tree had sensitivity of 96.2% and specificity of 21.3%. Conclusion: Though the decision trees were constructed with the best data in terms of initial triage and early secondary triage, the classification performance was limited. This highlights the difficulties of developing vital sign based triage tools to predict the need for LSI and survival.