Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands.
Department of Intensive Care, Maastricht University Medical Center, Maastricht, The Netherlands.
J Cereb Blood Flow Metab. 2020 Jan;40(1):135-149. doi: 10.1177/0271678X18806107. Epub 2018 Oct 24.
We analysed mean arterial blood pressure, cerebral blood flow velocity, oxygenated haemoglobin and deoxygenated haemoglobin signals to estimate dynamic cerebral autoregulation. We compared macrovascular (mean arterial blood pressure-cerebral blood flow velocity) and microvascular (oxygenated haemoglobin-deoxygenated haemoglobin) dynamic cerebral autoregulation estimates during three different conditions: rest, mild hypocapnia and hypercapnia. Microvascular dynamic cerebral autoregulation estimates were created by introducing the constant time lag plus constant phase shift model, which enables correction for transit time, blood flow and blood volume oscillations (TT-BF/BV correction). After TT-BF/BV correction, a significant agreement between mean arterial blood pressure-cerebral blood flow velocity and oxygenated haemoglobin-deoxygenated haemoglobin phase differences in the low frequency band was found during rest (left: intraclass correlation=0.6, median phase difference 29.5° vs. 30.7°, right: intraclass correlation=0.56, median phase difference 32.6° vs. 39.8°) and mild hypocapnia (left: intraclass correlation=0.73, median phase difference 48.6° vs. 43.3°, right: intraclass correlation=0.70, median phase difference 52.1° vs. 61.8°). During hypercapnia, the mean transit time decreased and blood volume oscillations became much more prominent, except for very low frequencies. The transit time related to blood flow oscillations was remarkably stable during all conditions. We conclude that non-invasive microvascular dynamic cerebral autoregulation estimates are similar to macrovascular dynamic cerebral autoregulation estimates, after TT-BF/BV correction is applied. These findings may increase the feasibility of non-invasive continuous autoregulation monitoring and guided therapy in clinical situations.
我们分析了平均动脉血压、脑血流速度、氧合血红蛋白和去氧血红蛋白信号，以估计动态脑自动调节。我们比较了三种不同条件下的宏观血管（平均动脉血压-脑血流速度）和微观血管（氧合血红蛋白-去氧血红蛋白）的动态脑自动调节估计值：休息、轻度低碳酸血症和高碳酸血症。通过引入常数时滞加常数相位偏移模型来创建微观血管动态脑自动调节估计值，该模型能够校正传输时间、血流和血液体积波动（TT-BF/BV 校正）。在 TT-BF/BV 校正后，在休息时（左侧：组内相关系数=0.6，中位数相位差 29.5°vs.30.7°，右侧：组内相关系数=0.56，中位数相位差 32.6°vs.39.8°）和轻度低碳酸血症（左侧：组内相关系数=0.73，中位数相位差 48.6°vs.43.3°，右侧：组内相关系数=0.70，中位数相位差 52.1°vs.61.8°），在低频带中发现了平均动脉血压-脑血流速度和氧合血红蛋白-去氧血红蛋白相位差之间的显著一致性。在高碳酸血症期间，除了非常低的频率外，平均传输时间减少，血液体积波动变得更加明显。在所有条件下，与血流波动相关的传输时间都非常稳定。我们得出结论，在应用 TT-BF/BV 校正后，非侵入性微观血管动态脑自动调节估计值与宏观血管动态脑自动调节估计值相似。这些发现可能会增加非侵入性连续自动调节监测和指导治疗在临床情况下的可行性。
