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.