Department of Cardiovascular Sciences, University of Leicester and NIHR Biomedical Research Centre, Leicester, UK.
Department of Kinesiology, Faculty of Medicine, and Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, QC, Canada.
J Cereb Blood Flow Metab. 2023 Jan;43(1):3-25. doi: 10.1177/0271678X221119760. Epub 2022 Aug 12.
Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.
脑自动调节(CA)是指脑组织血流(CBF)对灌注压变化的控制。由于颅内压测量的挑战,CA 通常被描述为平均动脉压(MAP)和 CBF 之间的关系。可以使用多种技术评估动态 CA(dCA),其中转移函数分析(TFA)最为常见。专注于 CA 的国际脑血管研究网络(CARNet)的成员在 2016 年发布了一份白皮书,旨在通过引入数据采集、分析和报告指南来提高 TFA 的标准化。此后,更多的证据使原始建议得以改进和完善,并纳入了新的指南,以反映该领域的最新进展。这份白皮书的第二版包含了更有力、基于证据的建议,并进行了扩展,以解决当前的研究趋势,包括优化 MAP 变异性、从替代方法获取 CBF 估计、估计替代 dCA 指标,以及将 dCA 量化纳入临床试验。实施这些新的和修订的建议对于提高 dCA 研究的可靠性和可重复性,促进机构间的合作以及比较研究结果非常重要。