Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
Journal
Physiological Measurement
Journal Volume
39
Journal Issue
12
Date Issued
2018
Author(s)
Sanders M.L.
Claassen J.A.H.R.
Aries M.
Bor-Seng-Shu E.
Caicedo A.
Chacon M.
Gommer E.D.
Van Huffel S.
Jara J.L.
Kostoglou K.
Mahdi A.
Marmarelis V.Z.
Mitsis G.D.
M?ller M.
Nikolic D.
Nogueira R.C.
Payne S.J.
Puppo C.
Shin D.C.
Simpson D.M.
Tarumi T.
Yelicich B.
Zhang R.
Panerai R.B.
Elting J.W.J.
Abstract
Objective: 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. Approach: 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). Main results: 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]). Significance: 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. ? 2018 Institute of Physics and Engineering in Medicine.
Subjects
Blood
Blood pressure
Correlation methods
Flow velocity
Hemodynamics
Cerebral autoregulation
Cerebral blood flow
Clinical use
Intraclass correlation coefficients
Method comparison
Multi methods
Protocol modeling
Reproducibilities
Surrogate data
Transfer function analysis
Systematic errors
aged
blood pressure measurement
brain circulation
clinical trial
female
homeostasis
human
male
multicenter study
reproducibility
Aged
Blood Pressure Determination
Cerebrovascular Circulation
Female
Homeostasis
Humans
Male
Reproducibility of Results
SDGs
Type
journal article