严重脑损伤预测的新方法。
Novel approaches to prediction in severe brain injury.
机构信息
Department of Neurology, Columbia University, New York, New York.
Departments of Anesthesiology & Critical Care Medicine, Neurology, Neurosurgery and Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
出版信息
Curr Opin Neurol. 2020 Dec;33(6):669-675. doi: 10.1097/WCO.0000000000000875.
PURPOSE OF REVIEW
Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science.
RECENT FINDINGS
Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques.
SUMMARY
Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.
目的综述
严重脑损伤后的恢复情况各不相同，在个体患者层面上准确预测具有挑战性。本综述重点介绍了临床预后预测的新进展，特别关注意识的预测以及对数据科学方法的日益依赖。
最近的发现
最近的研究利用血清生物标志物、定量脑电图、MRI 和生理时间序列来建立恢复预测模型。可以使用高效的计算技术来分析高分辨率数据和整合来自不同模态的特征。
总结
神经生理学和神经影像学的进步，结合计算方法，代表了一种预测严重脑损伤后意识和功能恢复的新范式。需要开展研究，以产生可靠的、针对个体患者的预测结果，从而对临床决策产生有意义的影响。
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