Jacob K Greenberg1, Margaret A Olsen2, Gabrielle W Johnson1, Ranbir Ahluwalia3, Madelyn Hill4, Andrew T Hale3, Ahmed Belal5, Shawyon Baygani5, Randi E Foraker2, Christopher R Carpenter6, Laurie L Ackerman5, Corina Noje7, Eric M Jackson8, Erin Burns9, Christina M Sayama9,10, Nathan R Selden9,10, Shobhan Vachhrajani4,11, Chevis N Shannon4, Nathan Kuppermann12,13, David D Limbrick1. 1. Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA. 2. Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA. 3. Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 4. Division of Neurosurgery, Dayton Children's Hospital, Dayton, Ohio, USA. 5. Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA. 6. Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA. 7. Department of Anesthesiology and Critical Care Medicine, Division of Pediatric Critical Care Medicine, The Charlotte R. Bloomberg Children's Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 8. Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland, USA. 9. Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA. 10. Department of Neurological Surgery, Oregon Health and Science University, Portland, Oregon, USA. 11. Department of Pediatrics, Wright State University, Dayton, Ohio, USA. 12. Department of Emergency Medicine, University of California Davis, School of Medicine, Sacramento, California, USA. 13. Department of Pediatrics, University of California Davis, School of Medicine, Sacramento, California, USA.
Abstract
BACKGROUND: When evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown. OBJECTIVE: To determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs. METHODS: We included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation >24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (<1%, <3%, and <5% predicted risk). RESULTS: The GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, 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%). CONCLUSION: Although measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model.
BACKGROUND: When evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown. OBJECTIVE: To determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs. METHODS: We included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation >24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (<1%, <3%, and <5% predicted risk). RESULTS: The GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, 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%). CONCLUSION: Although measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model.
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