This study aimed to further analyse the role of air medicine in traumatic brain injury (TBI) The authors used complex mathematical models of learning algorithms, such as artificial neural networks (ANN), support vector machines (SVM), and decision trees to identify relationships between data set variables.

Patients with Head Abbreviated Injury Score 3+ were identified from a local trauma registry. Predictive models were generated using the learning algorithms above. The three best-performing ANN models were used to calculate survival values (actual and predicted outcome) for each patient. In addition, predicted survival values with transport mode artificially input as "air" or "ground" were calculated for each patient to identify those who benefit from air transport.

The ratio of unexpected survivors to unexpected deaths for air- and ground-transported patients was compared using SVM. Finally, decision tree analysis was used to explore the indications for various transport modes in optimized survival algorithms.

A total of 11,961 patients were included. All three learning algorithms predicted a survival benefit with air transport across all patients, especially those with higher Head Abbreviated Injury Score or Injury Severity Score values, lower Glasgow Coma Scale scores, or hypotension. The authors conclude that air medical response in TBI seems to confer a survival advantage, especially in more critically injured patients.