  Vital signs and laboratory values are routinely used to guide clinical
decision-making for polytrauma patients, such as the decision to use damage
control techniques versus early definitive fracture fixation. Prior
multivariate models have tried to predict mortality risk, but due to several
limitations like one-time prediction at the time of admission, they have not
proven clinically useful. There is a need for a dynamic model that captures
evolving physiologic changes during patient's hospital course to trauma and
resuscitation for mortality prediction. The Parkland Trauma Index of Mortality
(PTIM) is a machine learning algorithm that uses electronic medical record
(EMR) data to predict $48-$hour mortality during the first $72$ hours of
hospitalization. The model updates every hour, evolving with the patient's
physiologic response to trauma. Area under (AUC) the receiver-operator
characteristic curve (ROC), sensitivity, specificity, positive (PPV) and
negative predictive value (NPV), and positive and negative likelihood ratios
(LR) were used to evaluate model performance. By evolving with the patient's
physiologic response to trauma and relying only on EMR data, the PTIM overcomes
many of the limitations of prior mortality risk models. It may be a useful tool
to inform clinical decision-making for polytrauma patients early in their
hospitalization.
