BACKGROUND: A quarter of all patients presenting to emergency departments are children. Although there are several large, well-conducted studies on adults enabling accurate selection of patients with head injury at high risk for computed tomography scanning, no such study has derived a rule for children. AIM: To conduct a prospective multicentre diagnostic cohort study to provide a rule for selection of high-risk children with head injury for computed tomography scanning. DESIGN: All children presenting to the emergency departments of 10 hospitals in the northwest of England with any severity of head injury were recruited. A tailor-made proforma was used to collect data on around 40 clinical variables for each child. These variables were defined from a literature review, and a pilot study was conducted before the children's head injury algorithm for the prediction of important clinical events (CHALICE) study. All children who had a clinically significant head injury (death, need for neurosurgical intervention or abnormality on a computed tomography scan) were identified. Recursive partitioning was used to create a highly sensitive rule for the prediction of significant intracranial pathology. RESULTS: 22,772 children were recruited over 2 1/2 years. 65% of these were boys and 56% were <5 years old. 281 children showed an abnormality on the computed tomography scan, 137 had a neurosurgical operation and 15 died. The CHALICE rule was derived with a sensitivity of 98% (95% confidence interval (CI) 96% to 100%) and a specificity of 87% (95% CI 86% to 87%) for the prediction of clinically significant head injury, and requires a computed tomography scan rate of 14%. CONCLUSION: A highly sensitive clinical decision rule is derived for the identification of children who should undergo computed tomography scanning after head injury. This rule has the potential to improve and standardise the care of children presenting with head injuries. Validation of this rule in new cohorts of patients should now be undertaken.
BACKGROUND: A quarter of all patients presenting to emergency departments are children. Although there are several large, well-conducted studies on adults enabling accurate selection of patients with head injury at high risk for computed tomography scanning, no such study has derived a rule for children. AIM: To conduct a prospective multicentre diagnostic cohort study to provide a rule for selection of high-risk children with head injury for computed tomography scanning. DESIGN: All children presenting to the emergency departments of 10 hospitals in the northwest of England with any severity of head injury were recruited. A tailor-made proforma was used to collect data on around 40 clinical variables for each child. These variables were defined from a literature review, and a pilot study was conducted before the children's head injury algorithm for the prediction of important clinical events (CHALICE) study. All children who had a clinically significant head injury (death, need for neurosurgical intervention or abnormality on a computed tomography scan) were identified. Recursive partitioning was used to create a highly sensitive rule for the prediction of significant intracranial pathology. RESULTS: 22,772 children were recruited over 2 1/2 years. 65% of these were boys and 56% were <5 years old. 281 children showed an abnormality on the computed tomography scan, 137 had a neurosurgical operation and 15 died. The CHALICE rule was derived with a sensitivity of 98% (95% confidence interval (CI) 96% to 100%) and a specificity of 87% (95% CI 86% to 87%) for the prediction of clinically significant head injury, and requires a computed tomography scan rate of 14%. CONCLUSION: A highly sensitive clinical decision rule is derived for the identification of children who should undergo computed tomography scanning after head injury. This rule has the potential to improve and standardise the care of children presenting with head injuries. Validation of this rule in new cohorts of patients should now be undertaken.
Authors: William R Mower; Jerome R Hoffman; Mel Herbert; Allan B Wolfson; Charles V Pollack; Michael I Zucker Journal: Ann Emerg Med Date: 2002-11 Impact factor: 5.721
Authors: Michael J Palchak; James F Holmes; Cheryl W Vance; Rebecca E Gelber; Bobbie A Schauer; Mathew J Harrison; Jason Willis-Shore; Sandra L Wootton-Gorges; Robert W Derlet; Nathan Kuppermann Journal: Ann Emerg Med Date: 2003-10 Impact factor: 5.721
Authors: Linda Papa; Manoj K Mittal; Jose Ramirez; Salvatore Silvestri; Philip Giordano; Carolina F Braga; Ciara N Tan; Neema J Ameli; Marco A Lopez; Crystal A Haeussler; Diego Mendez Giordano; Mark R Zonfrillo Journal: J Neurotrauma Date: 2017-04-18 Impact factor: 5.269
Authors: Jacob K Greenberg; Donna B Jeffe; Christopher R Carpenter; Yan Yan; Jose A Pineda; Angela Lumba-Brown; Martin S Keller; Daniel Berger; Robert J Bollo; Vijay M Ravindra; Robert P Naftel; Michael C Dewan; Manish N Shah; Erin C Burns; Brent R O'Neill; Todd C Hankinson; William E Whitehead; P David Adelson; Mandeep S Tamber; Patrick J McDonald; Edward S Ahn; William Titsworth; Alina N West; Ross C Brownson; David D Limbrick Journal: J Neurosurg Pediatr Date: 2018-10-26 Impact factor: 2.375
Authors: Silvia Bressan; Marco Daverio; Francesco Martinolli; Daniele Dona'; Federica Mario; Ivan P Steiner; Liviana Da Dalt Journal: Childs Nerv Syst Date: 2014-03 Impact factor: 1.475