Literature DB >> 28347684

Prognostic model for patients with traumatic brain injuries and abnormal computed tomography scans.

Jefferson Rosi Junior1, Leonardo C Welling2, Marcelo Schafranski3, Lin Tchia Yeng4, Rogério Ruscito do Prado5, Edwin Koterba6, Almir Ferreira de Andrade7, Manoel Jacobsen Teixeira8, Eberval Gadelha Figueiredo9.   

Abstract

Traumatic brain injury (TBI) is an important cause of death and disability worldwide. The prognosis evaluation is a challenge when many variables are involved. The authors aimed to develop prognostic model for assessment of survival chances after TBI based on admission characteristics, including extracranial injuries, which would allow application of the model before in-hospital therapeutic interventions. A cohort study evaluated 1275 patients with TBI and abnormal CT scans upon admission to the emergency unit of Hospital das Clinicas of University of Sao Paulo and analyzed the final outcome on mortality. A logistic regression analysis was undertaken to determine the adjusted weigh of each independent variable in the outcome. Four variables were found to be significant in the model: age (years), Glasgow Coma Scale (3-15), Marshall Scale (MS, stratified into 2,3 or 4,5,6; according to the best group positive predictive value) and anysochoria (yes/no). The following formula is in a logistic model (USP index to head injury) estimates the probability of death of patients according to characteristics that influence on mortality. We consider that our mathematical probability model (USP Index) may be applied to clinical prognosis in patients with abnormal CT scans after severe traumatic brain injury.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Brain injury; Prognostic; Survival

Mesh:

Year:  2017        PMID: 28347684     DOI: 10.1016/j.jocn.2017.03.012

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  3 in total

1.  Lesions in deep gray nuclei after severe traumatic brain injury predict neurologic outcome.

Authors:  Frédéric Clarençon; Éric Bardinet; Jacques Martinerie; Vincent Pelbarg; Nicolas Menjot de Champfleur; Rajiv Gupta; Eléonore Tollard; Gustavo Soto-Ares; Danielle Ibarrola; Emmanuelle Schmitt; Thomas Tourdias; Vincent Degos; Jérome Yelnik; Didier Dormont; Louis Puybasset; Damien Galanaud
Journal:  PLoS One       Date:  2017-11-02       Impact factor: 3.240

2.  Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Authors:  Cheng-Shyuan Rau; Pao-Jen Kuo; Peng-Chen Chien; Chun-Ying Huang; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  PLoS One       Date:  2018-11-09       Impact factor: 3.240

Review 3.  Therapeutic effect of intensive glycemic control therapy in patients with traumatic brain injury: A systematic review and meta-analysis of randomized controlled trials.

Authors:  Chunran Zhu; Jinjing Chen; Junchen Pan; Zhichao Qiu; Tao Xu
Journal:  Medicine (Baltimore)       Date:  2018-07       Impact factor: 1.889

  3 in total

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