Yang Zhihui, Xu Haiyan, Sura Livia, Arja Rawad Daniel, Patterson Robert Logan, Rossignol Candace, Albayram Mehmet, Rajderkar Dhanashree, Ghosh Suman, Wang Kevin, Weiss Michael D
Department of Emergency Medicine, University of Florida, 1149 Newell Drive, L3-166, Gainesville, FL, 32611, USA.
Department of Pediatrics, University of Florida, Gainesville, FL, 32610, USA.
Pediatr Res. 2023 Apr;93(5):1199-1207. doi: 10.1038/s41390-022-01994-0. Epub 2022 Mar 10.
Neuroprognostication in neonates with neonatal encephalopathy (NE) may be enhanced by early serial measurement of a panel of four brain-specific biomarkers.
To evaluate serum biomarkers, 40 NE samples and 37 healthy neonates from a biorepository were analyzed. Blood samples were collected at 0-6, 12, 24, 48, and 96 h of life. MRI provided a short-term measure of injury. Long-term outcomes included death or a Bayley III score at 17-24 months of age.
Glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase-L1 (UCH-L1), and Tau peaked at 0-6 h of life, while neurofilament light chain (NFL) peaked at 96 h of life. These four marker concentrations at 96 h of life differentiated moderate/severe from none/mild brain injury by MRI, while GFAP and Tau showed early discrimination. For long-term outcomes, GFAP, NFL, Tau, and UCH-L1 could differentiate a poor outcome vs good outcome as early as 0-6 h of life, depending on the Bayley domain, and a combination of the four markers enhanced the sensitivity and specificity. Machine learning trajectory analyses identified upward trajectory patients with a high concordance to poor outcomes.
GFAP, NFL, Tau, and UCH-L1 may be of neuroprognostic significance after NE.
Serial measurements of GFAP, NFL, Tau, and UCH-L1 show promise in aiding the bedside clinician in making treatment decisions in neonatal encephalopathy. The panel of four neuroproteins increased the ability to predict neurodevelopmental outcomes. The study utilized a trajectory analysis that enabled predictive modeling. A panel approach provides the bedside clinician with objective data to individualize care. This study provides the foundation to develop a point of care device in the future.
通过早期连续测量一组四种脑特异性生物标志物，可能会增强对患有新生儿脑病（NE）的新生儿的神经预后评估。
为了评估血清生物标志物，对生物样本库中的40份NE样本和37名健康新生儿进行了分析。在出生后0 - 6小时、12小时、24小时、48小时和96小时采集血样。MRI提供了损伤的短期测量指标。长期结局包括死亡或17 - 24个月龄时的贝利婴幼儿发展量表第三版（Bayley III）评分。
胶质纤维酸性蛋白（GFAP）、泛素C末端水解酶-L1（UCH-L1）和Tau蛋白在出生后0 - 6小时达到峰值，而神经丝轻链（NFL）在出生后96小时达到峰值。出生后96小时时这四种标志物的浓度通过MRI可区分中度/重度与无/轻度脑损伤，而GFAP和Tau蛋白显示出早期区分能力。对于长期结局，GFAP、NFL、Tau蛋白和UCH-L1早在出生后0 - 6小时就能根据贝利婴幼儿发展量表领域区分不良结局与良好结局，并且这四种标志物的组合提高了敏感性和特异性。机器学习轨迹分析确定了与不良结局高度一致的上升轨迹患者。
GFAP、NFL、Tau蛋白和UCH-L1在新生儿脑病后可能具有神经预后意义。
连续测量GFAP、NFL、Tau蛋白和UCH-L1有望帮助床边临床医生在新生儿脑病中做出治疗决策。这四种神经蛋白组合提高了预测神经发育结局的能力。该研究采用了轨迹分析，能够进行预测建模。组合方法为床边临床医生提供了客观数据以实现个性化护理。本研究为未来开发即时检测设备奠定了基础。
