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Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study
Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

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.

cerebral autoregulation, method comparison, reproducibility, surrogate data
0967-3334
Sanders, Marit L.
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Claassen, Jurgen A.H.R.
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Aries, Marcel
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Bor-Seng-Shu, Edson
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Caicedo, Alexander
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Chacon, Max
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Gommer, Erik D.
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Van Huffel, Sabine
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Jara, José L.
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Kostoglou, Kyriaki
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Mahdi, Adam
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Marmarelis, Vasilis Z.
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Mitsis, Georgios D.
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Müller, Martin
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Nikolic, Dragana
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Nogueira, Ricardo C.
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Payne, Stephen J.
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Puppo, Corina
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Shin, Dae C.
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Simpson, David M.
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Tarumi, Takashi
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Yelicich, Bernardo
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Zhang, Rong
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Panerai, Ronney B.
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Elting, Jan Willem J.
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Sanders, Marit L.
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Claassen, Jurgen A.H.R.
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Aries, Marcel
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Bor-Seng-Shu, Edson
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Caicedo, Alexander
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Chacon, Max
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Gommer, Erik D.
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Van Huffel, Sabine
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Jara, José L.
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Kostoglou, Kyriaki
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Mahdi, Adam
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Marmarelis, Vasilis Z.
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Mitsis, Georgios D.
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Müller, Martin
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Nikolic, Dragana
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Nogueira, Ricardo C.
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Payne, Stephen J.
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Puppo, Corina
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Shin, Dae C.
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Simpson, David M.
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Tarumi, Takashi
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Yelicich, Bernardo
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Zhang, Rong
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Panerai, Ronney B.
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Elting, Jan Willem J.
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Sanders, Marit L., Claassen, Jurgen A.H.R., Aries, Marcel, Bor-Seng-Shu, Edson, Caicedo, Alexander, Chacon, Max, Gommer, Erik D., Van Huffel, Sabine, Jara, José L., Kostoglou, Kyriaki, Mahdi, Adam, Marmarelis, Vasilis Z., Mitsis, Georgios D., Müller, Martin, Nikolic, Dragana, Nogueira, Ricardo C., Payne, Stephen J., Puppo, Corina, Shin, Dae C., Simpson, David M., Tarumi, Takashi, Yelicich, Bernardo, Zhang, Rong, Panerai, Ronney B. and Elting, Jan Willem J. (2018) Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study. Physiological Measurement, 39 (12), [125002]. (doi:10.1088/1361-6579/aae9fd).

Record type: Article

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.

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Reproducibility study CARNet manuscript revised - Accepted Manuscript
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Accepted/In Press date: 22 October 2018
e-pub ahead of print date: 22 October 2018
Published date: 7 December 2018
Keywords: cerebral autoregulation, method comparison, reproducibility, surrogate data

Identifiers

Local EPrints ID: 427889
URI: http://eprints.soton.ac.uk/id/eprint/427889
ISSN: 0967-3334
PURE UUID: 52fc67c5-d083-4e3d-840d-837eb94fc916

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Date deposited: 01 Feb 2019 17:30
Last modified: 26 Nov 2021 06:18

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Contributors

Author: Marit L. Sanders
Author: Jurgen A.H.R. Claassen
Author: Marcel Aries
Author: Edson Bor-Seng-Shu
Author: Alexander Caicedo
Author: Max Chacon
Author: Erik D. Gommer
Author: Sabine Van Huffel
Author: José L. Jara
Author: Kyriaki Kostoglou
Author: Adam Mahdi
Author: Vasilis Z. Marmarelis
Author: Georgios D. Mitsis
Author: Martin Müller
Author: Dragana Nikolic
Author: Ricardo C. Nogueira
Author: Stephen J. Payne
Author: Corina Puppo
Author: Dae C. Shin
Author: Takashi Tarumi
Author: Bernardo Yelicich
Author: Rong Zhang
Author: Ronney B. Panerai
Author: Jan Willem J. Elting

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