The significance of direct transportation to a trauma center on survival for severe traumatic brain injury

Perioperative management of adults with traumatic brain injury

Despite advances in management strategy, traumatic brain injury remains strongly associated with neurological impairment and mortality. Management of traumatic brain injury requires careful and targeted management of the physiological consequences which extend beyond the scope of the primary impact to the cranium. Here, we present a review of the principles of its acute management in adults. We outline the procedure which patients are assessed and the critical physiological variables which must be monitored to prevent further neurological damage. We describe current interventional strategies from the context of the underlying physiological mechanisms and recent clinical data and identify persisting challenges in traumatic brain injury management and potential avenues of future progress.

Trauma Anesthesiology Perioperative Management Update

Anesthesia for patients with life-threatening injuries is an essential part of post-accident care. Unfortunately, there is variability in trauma anesthesia care and numerous nonstandardized methods of working with patients remain. Uncertainty exists as to when and how best to intubate trauma patients, the use of vasopressors, and the appropriate management of severe traumatic brain injury. Some physicians recommend prehospital rapid sequence intubation, whereas others use bag-mask ventilation at lower pressures with no cricoid pressure and early transport to a trauma center. Overall, the absence of uniformity in trauma anesthesia care underlines the need for continued study and dialogue to define best practices and optimize patient outcomes.

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry

BACKGROUND

Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting.

METHODS

The Swedish Trauma Registry was used to train and validate five models - Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network - in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates.

RESULTS

There were 75,602 registrations between 2013-2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80-0.89 and AUCPR between 0.43-0.62.

CONCLUSIONS

AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.