论文标题

临床,生物学和计算观点的整合以支持大脑自动调节知情的临床决策,使使用机械时间尺度分解大脑自动调节以支持临床决策

Integration of Clinical, Biological, and Computational Perspectives to Support Cerebral Autoregulatory Informed Clinical Decision Making Decomposing Cerebral Autoregulation using Mechanistic Timescales to Support Clinical Decision-Making

论文作者

Briggs, J. K., Stroh, J. N., Bennett, T. D., Park, S., Albers, D. J.

论文摘要

适当的大脑功能和生命需要足够的大脑灌注。保持最佳的大脑灌注以避免继发性脑损伤是神经关怀的主要关注点之一。尽管压力危险,脑自动调节负责维持最佳的大脑灌注。对脑自动调节功能的了解应该是临床决策的关键因素,但通常不足以且应用不正确。多种生理机制会影响脑自动调节,每种生理机制都在潜在的不同且未完全理解的时间尺度上作用,从而使观察得出的结论混淆了。由于这种复杂性,大脑自动调节的临床概念化已被蒸馏成由多模式神经监控定义的实际指标,该指标可以消除机械信息并限制决策选项。下一步迈出了脑调节式临床决策的临床决策,是对脑自动调节进行机械量化,这需要将脑自动调节分解为其基本过程,并将这些过程分配到每个操作的时间标准中。在这篇综述中,我们在生物学,临床和以计算为重点的文献上对围绕大脑自动调节的基于时标的框架进行了仔细检查。这个新的框架将使我们能够量化机械相互作用,并直接推断哪些机制仅基于当前的监测设备起作用,这为大脑自动调节式临床临床决策的新边界铺平了道路。

Adequate brain perfusion is required for proper brain function and life. Maintaining optimal brain perfusion to avoid secondary brain injury is one of the main concerns of neurocritical care. Cerebral autoregulation is responsible for maintaining optimal brain perfusion despite pressure derangements. Knowledge of cerebral autoregulatory function should be a key factor in clinical decision-making, yet it is often insufficiently and incorrectly applied. Multiple physiologic mechanisms impact cerebral autoregulation, each of which operate on potentially different and incompletely understood timescales confounding conclusions drawn from observations. Because of such complexities, clinical conceptualization of cerebral autoregulation has been distilled into practical indices defined by multimodal neuromonitoring, which removes mechanistic information and limits decision options. The next step towards cerebral autoregulatory-informed clinical decision-making is to quantify cerebral autoregulation mechanistically, which requires decomposing cerebral autoregulation into its fundamental processes and partitioning those processes into the timescales at which each operates. In this review, we scrutinize biologically, clinically, and computationally focused literature to build a timescales-based framework around cerebral autoregulation. This new framework will allow us to quantify mechanistic interactions and directly infer which mechanism(s) are functioning based only on current monitoring equipment, paving the way for a new frontier in cerebral autoregulatory-informed clinical decision-making.

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