Junior Jefferson Rosi, Welling Leonardo C, Schafranski Marcelo, Yeng Lin Tchia, do Prado Rogério Ruscito, Koterba Edwin, de Andrade Almir Ferreira, Teixeira Manoel Jacobsen, Figueiredo Eberval Gadelha
Division of Neurological Surgery, University of Sao Paulo, Brazil.
Division of Neurological Surgery, State University of Ponta Grossa, Brazil.
J Clin Neurosci. 2017 Aug;42:122-128. doi: 10.1016/j.jocn.2017.03.012. Epub 2017 Mar 24.
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.
创伤性脑损伤(TBI)是全球范围内死亡和残疾的重要原因。当涉及许多变量时,预后评估是一项挑战。作者旨在基于入院特征(包括颅外损伤)开发一种用于评估TBI后生存几率的预后模型,这将允许在院内治疗干预之前应用该模型。一项队列研究评估了1275例入住圣保罗大学临床医院急诊科时CT扫描异常的TBI患者,并分析了死亡率的最终结果。进行了逻辑回归分析以确定每个自变量在结果中的调整权重。发现模型中有四个变量具有显著性:年龄(岁)、格拉斯哥昏迷量表(3 - 15)、马歇尔量表(MS,分为2、3或4、5、6;根据最佳组阳性预测值)和瞳孔不等大(是/否)。以下公式在逻辑模型(USP颅脑损伤指数)中根据影响死亡率的特征估计患者的死亡概率。我们认为我们的数学概率模型(USP指数)可应用于重度创伤性脑损伤后CT扫描异常患者的临床预后评估。