Trajectory Analysis of Serum Biomarker Concentrations Facilitates Outcome Prediction after Pediatric Traumatic and Hypoxemic Brain Injury
Trajectory Analysis of Serum Biomarker Concentrations Facilitates Outcome Prediction after Pediatric Traumatic and Hypoxemic Brain Injury
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DOI：
10.1159/000316803
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发表时间：
2010-01-01
影响因子：
2.9
通讯作者：
Fabio, Anthony
中科院分区：
文献类型：
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作者：
Berger, Rachel Pardes;Bazaco, Michael C.;Fabio, Anthony
创伤性脑损伤（TBI）和缺氧缺血性脑病（HIE）是儿童发病和死亡的主要原因。过去几年的几项研究评估了使用血清生物标志物来预测儿童脑损伤后的预后。这些研究都使用简单的点估计，如初始和峰值生物标志物浓度来预测结果。然而，这种方法不能识别随时间变化的模式。轨迹分析是一种可以捕捉生物标志物浓度随时间变化的分析类型，在社会科学中已获得成功。我们使用轨迹分析来评估3种脑特异性生物标志物（S100B、神经元特异性烯醇化酶（NSE）和髓鞘碱性蛋白（MBP））的血清浓度对儿童TBI和HIE后不良预后（格拉斯哥结局量表评分3-5分）的预测能力。对100例TBI或HIE患儿的临床和生物标志物数据进行了评估。对于每种生物标志物，我们使用敏感性和特异性验证了2、3和4组模型的结果预测。对于S100B， 3组模型预测预后不良，敏感性为59%，特异性为100%。对于NSE， 3组模型预测预后不良，敏感性为48%，特异性为98%。对于MBP， 3组模型预测预后不良，敏感性为73%，特异性为61%。因此，当模型预测出一个糟糕的结果时，出现糟糕结果的概率就非常高。相比之下，17%预后较差的受试者通过所有3种生物标志物轨迹预测预后良好。这些数据表明，生物标志物数据的轨迹分析可能为预测儿童脑损伤后的预后提供有用的方法。版权所有：S. Karger AG，巴塞尔
Traumatic brain injury (TBI) and hypoxic ischemic encephalopathy (HIE) are leading causes of morbidity and mortality in children. Several studies over the past several years have evaluated the use of serum biomarkers to predict outcome after pediatric brain injury. These studies have all used simple point estimates such as initial and peak biomarker concentrations to predict outcome. However, this approach does not recognize patterns of change over time. Trajectory analysis is a type of analysis which can capture variance in biomarker concentrations over time and has been used with success in the social sciences. We used trajectory analysis to evaluate the ability of the serum concentrations of 3 brain-specific biomarkers - S100B, neuron-specific enolase (NSE) and myelin basic protein (MBP) - to predict poor outcome (Glasgow Outcome Scale scores 3-5) after pediatric TBI and HIE. Clinical and biomarker data from 100 children with TBI or HIE were evaluated. For each biomarker, we validated 2-, 3- and 4-group models for outcome prediction, using sensitivity and specificity. For S100B, the 3-group model predicted poor outcome with a sensitivity of 59% and specificity of 100%. For NSE, the 3-group model predicted poor outcome with a sensitivity of 48% and specificity of 98%. For MBP, the 3-group model predicted poor outcome with a sensitivity of 73% and specificity of 61%. Thus, when the models predicted a poor outcome, there was a very high probability of a poor outcome. In contrast, 17% of subjects with a poor outcome were predicted to have a good outcome by all 3 biomarker trajectories. These data suggest that trajectory analysis of biomarker data may provide a useful approach for predicting outcome after pediatric brain injury. Copyright (C) 2010 S. Karger AG, Basel