A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.

Background: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusion (MT) and mortality. We describe a large-data machine learning-based Coagulation Risk Index (CRI) to test the early prediction of ATC in bleeding trauma patients.

Methods: Coagulation Risk Index was developed using continuous vital signs (VSs) available during the first 15 minutes after admission at a single trauma center over 4 years. Data to compute the CRI were derived from continuous features of photoplethymographic and electrocardiographic waveforms, oximetry values, and blood pressure trends. Two groups of patients at risk for ATC were evaluated: critical administration threshold and patients who received an MT. Acute traumatic coagulopathy was evaluated in separate models and defined as an international normalized ratio (INR) >1.2 and >1.5 upon arrival. The CRI was developed using 2 years of cases for training and 2 years for testing. The accuracy of the models is described by area under the receiver operator curve with 95% confidence intervals.

Results: A total of 17,567 patients were available for analysis with continuous VS data, 52.8% sustained blunt injury, 30.2% were female, and the mean age was 44.6 years. The ability of CRI to predict ATC in critical administration threshold patients was excellent. The true positive and true negative rates were 95.6% and 88.3%, and 94.9% and 89.2% for INR >1.2 and INR >1.5, respectively. The CRI also demonstrated excellent accuracy in patients receiving MT; true positive and true negative rates were 92.8% and 91.3%, and 100% and 88.1% for INR >1.2 and INR >1.5, respectively.

Conclusion: Using continuous VSs and large-data machine learning capabilities, the CRI accurately predicts early ATC in bleeding patients. Clinical application may guide early hemostatic resuscitation. Extension of this technology into the prehospital setting could provide earlier treatment of ATC.

Level Of Evidence: Retrospective, Prognostic Study; Level III.