Estimating confidence intervals for cerebral autoregulation: a parametric bootstrap approach.
Estimating confidence intervals for cerebral autoregulation: a parametric bootstrap approach.
复制标题
估计大脑自动调节的置信区间：参数引导方法。
DOI：
10.1088/1361-6579/ac27b8
复制
发表时间：
2021
影响因子：
3.2
通讯作者：
Bryant JED
中科院分区：
文献类型：
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作者：
Bryant JED
脑自动调节(CA)指的是脑血管系统在血压变化时控制血流的能力。评估脑自动调节的常用方法之一是由传递函数分析(TFA)得出的相位分析，该分析将动脉压(ABP)与脑血流量(CBF)联系起来。在比较受试者群体(如患者和健康对照或正常碳酸血症和高碳酸血症)时，这一指标和其他CA指标可以提供一致的结果，但在个体内部和个体之间可能会有很大的差异。本文的目的是提出一种新的参数Bootstrap方法，用于估计低频段平均相位估计的采样分布，从而估计平均相位估计的可信区间，以便优化CA函数的度量估计，并允许从单个记录中对CA的状态进行更稳健的推断。通过一组仿真实验，验证了该方法在受控条件下的有效性。20名健康成年志愿者(年龄25.53岁。分别用测距仪和经颅多普勒(应用于大脑中动脉)测量静息状态下的动脉血压和脑血流速度(CBFV)。对于每个志愿者，在不同的日期进行了五次单独的录音，每次大约18分钟长。使用TFA估计相。对记录数据的分析显示，在记录持续时间内，顺位系数变化很大，当噪声数据和低相干性频率被排除在分析之外时，这种变化可以减少(Wilcoxon符号秩检验法p=0.0065)。50秒的TFA窗口长度比100秒(P<；0.001)或20秒(P<；0.001)的长度更小，挑战了通常推荐的100秒。该方法为单个录音中的CA分析增加了一个急需的灵活统计工具。
Cerebral autoregulation (CA) refers to the ability of the brain vasculature to control blood flow in the face of changing blood pressure. One of the methods commonly used to assess cerebral autoregulation, especially in participants at rest, is the analysis of phase derived from transfer function analysis (TFA), relating arterial blood pressure (ABP) to cerebral blood flow (CBF). This and other indexes of CA can provide consistent results when comparing groups of subjects (eg patients and healthy controls or normocapnia and hypercapnia) but can be quite variable within and between individuals. The objective of this paper is to present a novel parametric bootstrap method, used to estimate the sampling distribution and hence confidence intervals (CIs) of the mean phase estimate in the low-frequency band, in order to optimise estimation of measures of CA function and allow more robust inferences on the status of CA from individual recordings. A set of simulations was used to verify the proposed method under controlled conditions. In 20 healthy adult volunteers (age 25.53. 5 years), ABP and CBF velocity (CBFV) were measured at rest, using a Finometer device and Transcranial Doppler (applied to the middle cerebral artery), respectively. For each volunteer, five individual recordings were taken on different days, each approximately 18 min long. Phase was estimated using TFA. Analysis of recorded data showed widely changing CIs over the duration of recordings, which could be reduced when noisy data and frequencies with low coherence were excluded from the analysis (Wilcoxon signed rank test p= 0.0065). The TFA window-lengths of 50s gave smaller CIs than lengths of 100s (p< 0.001) or 20s (p< 0.001), challenging the usual recommendation of 100s. The method adds a much needed flexible statistical tool for CA analysis in individual recordings.