Browsing by Subject "Minor head trauma"

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    Measures of Intracranial Injury Size Do Not Improve Clinical Decision Making for Children With Mild Traumatic Brain Injuries and Intracranial Injuries
    (Wolters Kluwer, 2022) Greenberg, Jacob K.; Olsen, Margaret A.; Johnson, Gabrielle W.; Ahluwalia, Ranbir; Hill, Madelyn; Hale, Andrew T.; Belal, Ahmed; Baygani, Shawyon; Foraker, Randi E.; Carpenter, Christopher R.; Ackerman, Laurie L.; Noje, Corina; Jackson, Eric M.; Burns, Erin; Sayama, Christina M.; Selden, Nathan R.; Vachhrajani, Shobhan; Shannon, Chevis N.; Kuppermann, Nathan; Limbrick, David D., Jr.; Neurological Surgery, School of Medicine
    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.