@article{ref1,
title="Models of mortality and morbidity in severe traumatic brain injury: an analysis of a Singapore neurotrauma database",
journal="World neurosurgery",
year="2017",
author="Han, Julian Xinguang and Qi See, Angela An and Gandhi, Mihir and Kam King, Nicolas Kon",
volume="108",
number="",
pages="885-893.e1",
abstract="OBJECTIVE: Current prognostic models for traumatic brain injury (TBI) available for use are developed from diverse historical datasets. We aim to construct a prognostication tool for severe TBI as it is this group who would benefit the most from an accurate model. <br><br>METHODS: Model development was based on a cohort of 300 patients with severe TBI (Glasgow Coma Score [GCS] ≤ 8) consecutively admitted to a neurosurgical intensive care unit in the National Neuroscience Institute (NNI), Singapore, between February 2006 and December 2009. We analyzed prospectively collected data of admission characteristics, using univariate and multivariate logistic regressions, to predict outcomes of mortality at 14 days and 6 months, and 6 month unfavourable outcome. Comparison with CRASH and IMPACT models was done using Akaike information criterion (AIC). <br><br>RESULTS: Two predictions models, NNI Clinical (age, GCS, pupillary reactivity) and NNI+ (Clinical model with the addition of obliteration of third ventricle or basal cisterns, presence of SDH, hypoxia and coagulopathy), were derived from this dataset. Both models predicted well across three outcome measures with AUC values ranging from.84 to.91, with adequate calibration. Comparison with the CRASH and IMPACT models showed better performance by both derived models with lower AIC and higher AUC values. <br><br>CONCLUSIONS: Two accurate prognostic models, NNI Clinical and NNI+, were developed from our cohort of severe TBI patients. Both models are specific to severe TBI and could be better alternatives to current available models. External validation will be required to assess our models' performance in a different setting.<br><br>Copyright © 2017 Elsevier Inc. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="1878-8750",
doi="10.1016/j.wneu.2017.08.147",
url="http://dx.doi.org/10.1016/j.wneu.2017.08.147"
}