Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

Background

Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging, particularly computed tomography (CT) scans, presents risks including radiation-induced carcinogenesis. In this study we leverage machine learning methods to develop highly accurate clinical decision rules to predict pediatric CSI.

Methods

The PEDSPINE I registry was used to investigate CSI in blunt trauma patients under the age of three. Predictive models were built using Optimal Classification Trees, a novel machine learning approach offering high accuracy and interpretability, as well as other widely used machine learning methods.

Results

The final Optimal Classification Trees model predicts injury based on overall Glasgow Coma Score (GCS) and patient age. This model has a sensitivity of 93.3% and specificity of 82.3% on the full dataset. It has comparable or superior performance to other machine learning methods as well as existing clinical decision rules.

Conclusions

This study developed a decision rule that achieves high injury identification while reducing unnecessary imaging. It demonstrates the value of machine learning in improving clinical decision protocols for pediatric trauma.

Type of study

Retrospective Prognosis Study.

Level of evidence

II