133 Quantitative Imaging Measurements Marginally Improve Risk Prediction for Children with Mild Traumatic Brain Injuries and Intracranial Injuries. (1st April 2022)

• Record Type: Journal Article
• Authors: Greenberg, Jacob K.; Olsen, Margaret; Johnson, Gabbie; Ahluwalia, Ranbir; Hill, Madelyn; Hale, Andrew T.; Belal, Ahmed M. A.; Baygani, Shawyon; Foraker, Randi; Carpenter, Christopher; Ackerman, Laurie; Noje, Corina; Jackson, Eric; Burns, Erin; Sayama, Christina M.; Selden, Nathan R.; Vachhrajani, Shobhan H.; Shannon, Chevis; Kuppermann, Nathan; Limbrick, David D.
• Is Part Of: Neurosurgery. Volume 68(2022)Supplement 1
• Page Start: 38
• Page End: 39
• Publication Date: 2022-04-01
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1227/NEU.0000000000001880_133
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : INTRODUCTION: The KIIDS-TBI model was recently developed and externally validated in two large, multicenter populations to risk-stratify children with mild traumatic brain injuries (mTBI) and intracranial injuries. However, the KIIDS-TBI model only evaluates the presence/absence of imaging findings, rather than quantitative measures (e.g., hematoma size). METHODS: We included children <18 years who presented to one of five centers within 24 hours of TBI, had GCS scores of 13-15, and had intracranial injuries on neuroimaging. The dataset was split into training (75%) and test (25%) cohorts. We used generalized linear models (GLM) and recursive partitioning (RP) to predict the composite outcome of neurosurgery, intubation >24 hours due to TBI, or death due to TBI. Each model's performance was compared to the KIIDS-TBI model across three decision-making risk cutoffs (<1%, <3%, and <5% predicted risk). RESULTS: There were 1, 126 children included in the training and 374 in the test dataset. The RP model included epidural hematoma size, amount of skull fracture depression, amount of midline shift, and extra-axial hematoma size. The GLM model included these imaging variables and added clinical predictors, such as GCS Score. The GLM (76-90%) and RP (79-87%) models showed similar specificity across the three risk cutoffs in the test dataset, but the GLM model had higher sensitivity (89-96% for GLM; 89% for RP). By comparison, the KIIDS-TBI model had slightly higher sensitivity (93-100%) but lower specificity (27-82%) across the three risk cutoffs. CONCLUSION: Despite having obvious face value in many aspects of neurosurgical decision-making, quantitative imaging measures only marginally improved efforts to identify low risk patients in a large dataset of children with mTBI and intracranial injuries. Given the tradeoff between improved specificity but decreased sensitivity, these results do not support the addition of quantitative imaging measures to the KIIDS-TBI tool.