Parkland Trauma Index of Mortality (PTIM): Real-time Predictive Model for PolyTrauma Patients
This addresses the need for real-time mortality prediction to inform clinical decisions for polytrauma patients, though it is incremental as it builds on prior models by adding dynamic updates.
The authors tackled the problem of predicting mortality in polytrauma patients by developing a dynamic machine learning model (PTIM) that updates hourly using EMR data, achieving performance metrics like AUC, sensitivity, and specificity for 48-hour mortality prediction within the first 72 hours of hospitalization.
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.