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基于液体的创伤性脑损伤蛋白生物标志物:来自床边的观察。

Fluid-Based Protein Biomarkers in Traumatic Brain Injury: The View from the Bedside.

机构信息

Department of Anatomy, Physiology and Genetic, School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA.

Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.

出版信息

Int J Mol Sci. 2023 Nov 13;24(22):16267. doi: 10.3390/ijms242216267.

Abstract

There has been an explosion of research into biofluid (blood, cerebrospinal fluid, CSF)-based protein biomarkers in traumatic brain injury (TBI) over the past decade. The availability of very large datasets, such as CENTRE-TBI and TRACK-TBI, allows for correlation of blood- and CSF-based molecular (protein), radiological (structural) and clinical (physiological) marker data to adverse clinical outcomes. The quality of a given biomarker has often been framed in relation to the predictive power on the outcome quantified from the area under the Receiver Operating Characteristic (ROC) curve. However, this does not in itself provide utility but reflects a statistical association in any given population between one or more variables and clinical outcome. It is not currently established how to incorporate and integrate biofluid-based biomarker data into patient management because there is no standardized role for such data in clinical decision making. We review the current status of biomarker research and discuss how we can integrate existing markers into current clinical practice and what additional biomarkers do we need to improve diagnoses and to guide therapy and to assess treatment efficacy. Furthermore, we argue for employing machine learning (ML) capabilities to integrate the protein biomarker data with other established, routinely used clinical diagnostic tools, to provide the clinician with actionable information to guide medical intervention.

摘要

在过去的十年中,生物流体(血液、脑脊液)中的蛋白质生物标志物在创伤性脑损伤(TBI)方面的研究呈爆炸式增长。CENTRE-TBI 和 TRACK-TBI 等大型数据集的出现,使得血液和脑脊液中的分子(蛋白质)、影像学(结构)和临床(生理)标志物数据与不良临床结果相关联成为可能。给定生物标志物的质量通常与从接收者操作特征(ROC)曲线下面积量化的预测能力有关。然而,这本身并不能提供实用性,而是反映了在任何特定人群中,一个或多个变量与临床结果之间的统计关联。目前还没有确定如何将基于生物流体的生物标志物数据纳入患者管理,因为此类数据在临床决策中没有标准化的作用。我们回顾了生物标志物研究的现状,并讨论了如何将现有标志物纳入当前的临床实践,以及我们还需要哪些额外的标志物来改善诊断、指导治疗和评估治疗效果。此外,我们主张利用机器学习(ML)能力将蛋白质生物标志物数据与其他已建立的、常规使用的临床诊断工具相结合,为临床医生提供可操作的信息,以指导医疗干预。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/733b/10671762/9edc0a32a881/ijms-24-16267-g001.jpg

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