TY - JOUR
T1 - 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
AU - Rowe, Callum
AU - Wiesendanger, Kathryn
AU - Polet, Conner
AU - Kuppermann, Nathan
AU - Aronoff, Stephen
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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 dataset. Study design: The deidentified dataset consisted of 43 399 patients <18 years old who presented with blunt head trauma to 1 of 25 pediatric emergency departments between June 2004 and September 2006. We divided the dataset into derivation (training) and validation (testing) subsets; 4 machine learning algorithms were optimized using the training set. Fitted models used the test set to predict ciTBI and these predictions were compared statistically with the a priori (no information) rate. Results: None of the 4 machine learning models was superior to the no information rate. Children without clinical evidence of a skull fracture and with Glasgow Coma Scale scores of 15 were at the lowest risk for ciTBIs (0.48%; 95% CI 0.42%-0.55%). Conclusions: 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 clinical evidence of a skull fractures and having Glasgow Coma Scale scores of 15.
AB - 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 dataset. Study design: The deidentified dataset consisted of 43 399 patients <18 years old who presented with blunt head trauma to 1 of 25 pediatric emergency departments between June 2004 and September 2006. We divided the dataset into derivation (training) and validation (testing) subsets; 4 machine learning algorithms were optimized using the training set. Fitted models used the test set to predict ciTBI and these predictions were compared statistically with the a priori (no information) rate. Results: None of the 4 machine learning models was superior to the no information rate. Children without clinical evidence of a skull fracture and with Glasgow Coma Scale scores of 15 were at the lowest risk for ciTBIs (0.48%; 95% CI 0.42%-0.55%). Conclusions: 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 clinical evidence of a skull fractures and having Glasgow Coma Scale scores of 15.
UR - http://www.scopus.com/inward/record.url?scp=85086525052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086525052&partnerID=8YFLogxK
U2 - 10.1016/j.ympdx.2020.100026
DO - 10.1016/j.ympdx.2020.100026
M3 - Article
AN - SCOPUS:85086525052
VL - 3
JO - Journal of Pediatrics: X
JF - Journal of Pediatrics: X
SN - 2590-0420
M1 - 100026
ER -