Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study

Background: This study aimed to develop a model for predicting trauma outcomes by adding arterial lactate levels measured upon emergency room (ER) arrival to existing trauma injury severity scoring systems. Methods: We examined blunt trauma cases that were admitted to our hospital during 2010-2014. Eligibility criteria were cases with an Injury Severity Score of >= 9, complete Trauma and Injury Severity Score (TRISS) variable data, and lactate levels that were assessed upon ER arrival. Survivor and non-survivor groups were compared and lactate-based prediction models were generated using logistic regression. We compared the predictive performances of traditional prediction models (Revised Trauma Score [RTS] and TRISS) and lactate-based models using the area under the curve (AUC) of receiver operating characteristic curves. Results: We included 829 patients, and the in-hospital mortality rate among these patients was 21.6%. The model that used lactate levels and age provided a significantly better AUC value than the RTS model. The model with lactate added to the TRISS variables provided the highest Youden J statistic, with 86.0% sensitivity and 70.8% specificity at a cutoff value of 0.15, as well as the highest predictive value, with a significantly higher AUC than the TRISS. Conclusions: These findings indicate that lactate testing upon ER arrival may help supplement or replace traditional physiological parameters to predict mortality outcomes among Korean trauma patients. Adding lactate levels also appears to improve the predictive abilities of existing trauma outcome prediction models.