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颅脑损伤的多变量结局预测,重点关注实验室指标。

Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

机构信息

Department of Physiology and Pharmacology, Section of Anesthesiology and Intensive Care, Karolinska Institutet, Stockholm, Sweden.

出版信息

J Neurotrauma. 2012 Nov 20;29(17):2613-24. doi: 10.1089/neu.2012.2468. Epub 2012 Nov 14.

Abstract

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².

摘要

创伤性脑损伤(TBI)是发病率和死亡率的主要原因。确定与结果相关的因素可以更好地了解 TBI 的病理生理学,除了有助于预后判断。许多常见的实验室变量与结果相关,但在多变量环境下可能不是独立的预测因素。在这项研究中,在 Karolinska TBI 数据库中确定了 757 名可检索到早期实验室变量的患者。使用逻辑回归和非线性机器学习方法——相关向量机对这些变量进行了分析,针对格拉斯哥结局量表(GOS)进行了二分类,并对 TBI 结果中的已知重要预测因素(年龄、格拉斯哥昏迷评分(GCS)、瞳孔反应和计算机断层扫描(CT)评分)进行了单变量和控制分析。准确性通过 Nagelkerke 的伪 R²进行评估。在研究的 18 个实验室变量中,有 15 个在单变量分析中对结果有显著意义(p<0.05)。相比之下,当调整其他预测因素时,很少有变量仍然具有显著意义。肌酐被发现是 TBI 结果的独立预测因素。血糖、白蛋白和渗透压水平也被确定为预测因素,具体取决于分析方法。渗透压升高与预后不良相关,这可能需要进一步研究。重要的是,血红蛋白在调整后的 GCS 为复苏后 GCS 而不是入院 GCS 时没有显著意义,因此 GCS 的时间可能对结论有重大影响。总的来说,实验室变量使伪 R²增加了 1.3-4.4%。

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