Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Safar Center for Resuscitation Research, Pittsburgh, PA 15227, USA.
Dev Neurosci. 2010;32(5-6):396-405. doi: 10.1159/000316803. Epub 2010 Sep 18.
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.
创伤性脑损伤 (TBI) 和缺氧缺血性脑病 (HIE) 是导致儿童发病率和死亡率的主要原因。过去几年的几项研究评估了使用血清生物标志物预测儿童脑损伤后的结果。这些研究都使用了初始和峰值生物标志物浓度等简单的点估计来预测结果。然而，这种方法没有认识到随时间变化的模式。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方法，已成功应用于社会科学。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））的血清浓度预测儿童 TBI 和 HIE 后不良结局（格拉斯哥结局量表评分 3-5）的能力。评估了 100 名患有 TBI 或 HIE 的儿童的临床和生物标志物数据。对于每种生物标志物，我们使用灵敏度和特异性验证了用于预测结果的 2-、3-和 4-组模型。对于 S100B，3 组模型预测不良结局的灵敏度为 59%，特异性为 100%。对于 NSE，3 组模型预测不良结局的灵敏度为 48%，特异性为 98%。对于 MBP，3 组模型预测不良结局的灵敏度为 73%，特异性为 61%。因此，当模型预测不良结局时，不良结局的可能性非常高。相比之下，3 种生物标志物轨迹预测 17%的不良结局患者有良好的结局。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结果提供一种有用的方法。
