@article{ref1,
title="Epidemiology of mild traumatic brain injury with intracranial hemorrhage: focusing predictive models for neurosurgical intervention",
journal="World neurosurgery",
year="2017",
author="Orlando, Alessandro and Levy, A. Stewart and Carrick, Matthew M. and Tanner, Allen and Mains, Charles W. and Bar-Or, David",
volume="107",
number="",
pages="94-102",
abstract="OBJECTIVE: To adumbrate differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries (mTBIs), and help identify which ICH types are most likely to benefit from the creation of predictive models for NI. <br><br>METHODS: This was a multi-center retrospective study of adult patients over three years at four Trauma Centers in the USA. Patients were included if they presented with a mTBI (GCS 13-15) and a head CT positive for ICH. Patients were excluded for skull fractures, &quot;unspecified hemorrhage&quot;, or coagulopathy. The primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. <br><br>RESULTS: 1,876 patients were included in our study. The NI rate was 6.7%. There was a significant difference in the rate of NI by ICH type. Subdural hematomas carried the highest rate of NI (15.5%), and accounted for 78% of all NIs. Isolated SAHs carried the lowest, non-zero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated SAHs would require 26,928 patients, but a model predicting NI for isolated SDHs would only require 328 patients. <br><br>CONCLUSION: Our study highlights the disparate NI rates among ICH types in the mTBI population, and identified mild, isolated SDHs as most appropriate for the construction of predictive NI models. Increased healthcare efficiency will be driven by an accurate understanding of risk, which can only come from accurate predictive models.<br><br>Copyright © 2017 Elsevier Inc. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="1878-8750",
doi="10.1016/j.wneu.2017.07.130",
url="http://dx.doi.org/10.1016/j.wneu.2017.07.130"
}