Derivation and Validation of a Simplified Clinical Prediction Rule for Identifying Children at Increased Risk for Clinically Important Traumatic Brain Injuries Following Minor Blunt Head Trauma

Abstract Objective To develop a simplified clinical prediction tool for identifying children with clinically-important traumatic brain injuries (ciTBIs) after minor blunt head trauma by applying machine learning to the previously reported Pediatric Emergency Care Applied Research Network (PECARN) dataset. Study design The deidentified dataset consisted of 43,399 patients Results None of the four machine learning models was superior to the no information rate. Children without clinical evidence of a skull fracture and with GCS scores of 15 were at the lowest risk for ciTBIs (0.48%; 95% CI: 0.42,0.55%). Conclusion Machine learning algorithms were unable to produce a more accurate prediction tool for ciTBI among children with minor blunt head trauma beyond the absence of skull fracture and having GCS scores of 15.