Informatics, Decision-Enhancement and Analytic Sciences Center, VA Salt Lake City, Salt Lake City, Utah, USA.
Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
J Neurotrauma. 2021 Dec;38(23):3222-3234. doi: 10.1089/neu.2021.0059. Epub 2021 Jun 10.
It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably referred to as subtypes, phenotypes, and/or clinical profiles. As part of the Brain Trauma Blueprint TBI State of the Science, we review the current literature on TBI phenotyping with an emphasis on unsupervised methodological approaches, and describe five phenotypes that appear similar across reports. However, we also find the literature contains divergent analysis strategies, inclusion criteria, findings, and use of terms. Further, whereas some studies delineate phenotypes within a specific severity of TBI, others derive phenotypes across the full spectrum of severity. Together, these facts confound direct synthesis of the findings. To overcome this, we introduce PhenoBench, a freely available code repository for the standardization and evaluation of raw phenotyping data. With this review and toolset, we provide a pathway toward robust, data-driven phenotypes that can capture the heterogeneity of TBI, enabling reproducible insights and targeted care.
人们普遍认识到,创伤性脑损伤(TBI)的范围从轻度到重度,具有不同的临床表现,这些表现通常被称为亚型、表型和/或临床特征。作为脑创伤蓝图 TBI 科学现状的一部分,我们回顾了 TBI 表型的当前文献,重点介绍了无监督的方法,并描述了五个在报告中似乎相似的表型。然而,我们也发现文献中包含了不同的分析策略、纳入标准、发现和术语使用。此外,虽然有些研究在特定严重程度的 TBI 内描述了表型,但其他研究则跨越了整个严重程度范围来得出表型。这些事实使得研究结果难以直接综合。为了克服这一问题,我们引入了 PhenoBench,这是一个免费的代码库,用于对原始表型数据进行标准化和评估。通过本综述和工具集,我们提供了一种途径来实现稳健的、数据驱动的表型,这些表型可以捕捉 TBI 的异质性,从而实现可重复的见解和有针对性的治疗。