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

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

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 desi...

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Main Authors: Callum Rowe, MD, Kathryn Wiesendanger, BS, Conner Polet, MD, Nathan Kuppermann, MD, MPH, Stephen Aronoff, MD, MBA
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:The Journal of Pediatrics: X
Online Access:http://www.sciencedirect.com/science/article/pii/S2590042020300070
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spelling doaj-57f169ed0c4b4d1399d82afde1fc17082020-11-25T03:07:22ZengElsevierThe Journal of Pediatrics: X2590-04202020-01-013100026Derivation and Validation of a Simplified Clinical Prediction Rule for Identifying Children at Increased Risk for Clinically Important Traumatic Brain Injuries Following Minor Blunt Head TraumaCallum Rowe, MD0Kathryn Wiesendanger, BS1Conner Polet, MD2Nathan Kuppermann, MD, MPH3Stephen Aronoff, MD, MBA4Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CADepartment of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CADepartment of Psychiatry, SUNY Downstate, New York, NYDepartment of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CADepartment of Pediatrics, Lewis Katz School of Medicine at Temple University, Philadelphia, PA; Reprint requests: Stephen Aronoff, MD, MBA, Department of Pediatrics, 2nd floor Kresge Building, West Wing, 3440 N. Broad St, Philadelphia, PA 191940.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 skull fractures and having Glasgow Coma Scale scores of 15.http://www.sciencedirect.com/science/article/pii/S2590042020300070
collection DOAJ
language English
format Article
sources DOAJ
author Callum Rowe, MD
Kathryn Wiesendanger, BS
Conner Polet, MD
Nathan Kuppermann, MD, MPH
Stephen Aronoff, MD, MBA
spellingShingle Callum Rowe, MD
Kathryn Wiesendanger, BS
Conner Polet, MD
Nathan Kuppermann, MD, MPH
Stephen Aronoff, MD, MBA
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
The Journal of Pediatrics: X
author_facet Callum Rowe, MD
Kathryn Wiesendanger, BS
Conner Polet, MD
Nathan Kuppermann, MD, MPH
Stephen Aronoff, MD, MBA
author_sort Callum Rowe, MD
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher Elsevier
series The Journal of Pediatrics: X
issn 2590-0420
publishDate 2020-01-01
description 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 skull fractures and having Glasgow Coma Scale scores of 15.
url http://www.sciencedirect.com/science/article/pii/S2590042020300070
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