Prediction of outcome in traumatic brain injury with computed tomographic characteristics: A comparison between the computed tomographic classification and combinations of computed tomographic predictors
Prediction of outcome in traumatic brain injury with computed tomographic characteristics: A comparison between the computed tomographic classification and combinations of computed tomographic predictors
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DOI：
10.1227/neu.0000186013.6304668
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发表时间：
2005-12-01
期刊：
影响因子：
4.8
通讯作者：
Steyerberg, EW
中科院分区：
文献类型：
--
作者：
Maas, AIR;Hukkelhoven, CWPM;Steyerberg, EW
背景与目的：马歇尔计算机断层扫描（CT）分类法根据CT扫描的形态学异常将创伤性脑损伤（TBI）患者分为六组。这种分类越来越多地被用作结果的预测因素。我们的目的是检查的预测价值的马歇尔CT分类比较替代CT models.METHODS：的预测价值进行了研究，在Tirilazad试验（n = 2269）。采用逻辑回归分析和递归分割开发了替代模型。6个月的死亡率被用作结局指标。内部效度用自举技术进行评估，并表示为受试者工作曲线下面积（AUC）。结果：马歇尔CT分类表明合理的歧视（AUC = 0.67），这可以通过重新安排潜在的个人CT特征（AUC = 0.71）得到改善。通过增加脑室内和创伤性蛛网膜下腔出血，以及更详细地区分肿块病变和基底脑池，可以进一步提高性能（AUC = 0.77）。逻辑回归分析和递归分区开发的模型表现出类似的性能。对于临床应用，我们提出了一个简单的CT评分，它允许一个更明确的分化的预后风险，特别是在患者肿块insure.CONCLUSION：它是最好的组合使用的个人CT预测，而不是马歇尔CT分类TBI的预后目的。这种模型至少应包括以下参数：基底池的状态、移位、创伤性蛛网膜下腔或脑室内出血，以及不同类型的肿块病变的存在。
BACKGROUND AND OBJECTIVE: The Marshall computed tomographic (CT) classification identifies six groups of patients with traumatic brain injury (TBI), based on morphological abnormalities on the CT scan. This classification is increasingly used as a predictor of outcome. We aimed to examine the predictive value of the Marshall CT classification in comparison with alternative CT models.METHODS: The predictive value was investigated in the Tirilazad trials (n = 2269). Alternative models were developed with logistic regression analysis and recursive partitioning. Six month mortality was used as outcome measure. Internal validity was assessed with bootstrapping techniques and expressed as the area under the receiver operating curve (AUC).RESULTS: The Marshall CT classification indicated reasonable discrimination (AUC = 0.67), which could be improved by rearranging the underlying individual CT characteristics (AUC = 0.71). Performance could be further increased by adding intraventricular and traumatic subarachnoid hemorrhage and by a more detailed differentiation of mass lesions and basal cisterns (AUC = 0.77). Models developed with logistic regression analysis and recursive partitioning showed similar performance. For clinical application we propose a simple CT score, which permits a more clear differentiation of prognostic risk, particularly in patients with mass lesions.CONCLUSION: It is preferable to use combinations of individual CT predictors rather than the Marshall CT classification for prognostic purposes in TBI. Such models should include at least the following parameters: status of basal cisterns, shift, traumatic subarachnoid or intraventricular hemorrhage, and presence of different types of mass lesions.