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Keeping it simple: the value of mortality prediction after trauma with basic indices like the Reverse Shock Index
Sven Frieler1, Rolf Lefering2, Julius Gerstmeyer3
1Department of General and Trauma Surgery, BG University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany sven.frieler@bergmannsheil.de.
Background:
Identification of trauma patients at significant risk of death in the prehospital setting is challenging. The prediction probability of basic indices like vital signs, Shock Index (SI), SI multiplied by age (SIA) or the GCS is limited and more complex scores are not feasible on-scene. The Reverse SI multiplied by GCS score (rSIG) has been proposed as a triage tool to identify trauma patients with an increased risk of dying at EDs. Age adjustment (rSIG/A) displayed no advantage.We aim to (1) validate the accuracy of the rSIG in predicting death or early transfusion in a large trauma registry population, and (2) determine if the rSIG is valid for evaluation of trauma patients in the prehospital setting.
Methods:
70 829 trauma patients were retrieved from the TraumaRegister DGU database (time period between 2008 and 2017). The area under the receiver operating characteristic curve (AUROC) was calculated to measure the ability of SI, SIA, rSIG and rSIG divided by age (rSIG/A) to predict in-hospital mortality from data at the time of hospital arrival and solely from prehospital data.
Results:
The rSIG at time of hospital admission was not sufficiently predictive for clinical decision-making. However, rSIG calculated solely from prehospital data accurately predicted risk of death. Using prehospital data, the AUROC for mortality of rSIG/A was the highest (0.85; CI: 0.85 to 0.86), followed by rSIG (0.76; CI: 0.75 to 0.77), SIA (0.71; CI: 0.70 to 0.71) and SI (0.48; CI: 0.47 to 0.49).
Conclusion:
The prehospital rSIG/A can be a useful adjunct for the prehospital evaluation of trauma patients and their allocation to trauma centres or trauma team activation. However, we could not confirm that the rSIG at hospital admission is a reliable tool for risk stratification.
