Department of Geriatric Medicine, Radboudumc Alzheimer Centre and Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Physiol Meas. 2018 Dec 7;39(12):125002. doi: 10.1088/1361-6579/aae9fd.
Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques.
Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC).
For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]).
When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.
目前已有多种计算动态脑自动调节（dCA）参数的方法，但这些方法的重复性大多较差，限制了其在临床应用中的可靠性。研究方案、建模方法和默认参数设置等方面的中心间差异，导致研究之间缺乏标准化和可比性。本研究通过评估不同建模技术产生的替代数据中的系统误差，来评估 dCA 参数的可重复性。
14 个中心分析了 22 个数据集，这些数据集由两次重复的生理血压测量和使用 Tiecks 曲线（自动调节指数，ARI0-9）生成的替代脑血流速度信号组成，并添加了噪声。为了评估重复性，将 dCA 方法分为三大类：1. 传递函数分析（TFA）类输出；2. ARI 类输出；3. 相关系数类输出。对于所有方法，均通过单向组内相关系数分析（ICC）确定可重复性。
对于 TFA 类方法，增益的 ICC 均值（标准差；[范围]）分别为 0.71（0.10；[0.49-0.86]）和 0.80（0.17；[0.36-0.94]），低频（LF）和甚低频（VLF）时分别为 0.003 和 0.003。对于相位，VLF 时 ICC 值为 0.53（0.21；[0.09-0.80]），LF 时 ICC 值为 0.92（0.13；[0.44-1.00]），p<0.001。最后，ARI 类方法的 ICC 为 0.84（0.19；[0.41-0.94]），相关类方法的 ICC 为 0.21（0.21；[0.056-0.35]）。
当应用于现实的替代数据时，这些数据不受生理变异性对脑血流的额外外源影响，大多数 dCA 建模方法的 ICC 值明显高于生理数据的报告值。这一发现表明，以前研究报告的可重复性差可能主要是由于脑血流调节机制的固有生理变异性，而不是与（静态）随机噪声和信号分析方法有关。
