Parkland Trauma Index of Mortality (PTIM): Real-time Predictive Model for PolyTrauma Patients

TL;DR

The paper introduces PTIM, a real-time machine learning model that predicts 48-hour mortality in polytrauma patients by continuously updating with physiological data during hospitalization.

Contribution

It presents a dynamic, hourly-updating mortality prediction model using EMR data, overcoming limitations of previous static models.

Findings

Model achieved high AUC in predicting mortality

Evolved with patient's physiologic response

Potential to inform early clinical decisions

Abstract

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.