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.
By Adam J. Starr, Manjula Julka, Arun Nethi, John D. Watkins, Ryan W. Fairchild, Michael W. Cripps, Dustin Rinehart, Hayden N. Box
