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Project 1: Variability of cerebral autoregulation

In this project, we investigated the variability that is found between different centres when performing what should be the same analysis. We looked at the different parameters recovered by different centres when carrying out Transfer Function Analysis (TFA) on a common, pooled, set of experimental data. The results can be seen in [1]. As a result of this, a common standard for performing TFA has been proposed [2] and this has now become widely adopted by the community. Code for this analysis is publicly available on the CARNet website.
 

  1. A. S. S. Meel-van den Abeelen, D. M. Simpson, L. J. Y. Wang, C. H. Slump, R. Zhang, T. Tarumi, C. A. Rickards, S. Payne, G. D. Mitsis, K. Kostoglou, V. Marmarelis, D. Shin, Y. C. Tzeng, P. N. Ainslie, E. Gommer, M. Muller, A. C. Dorado, P. Smielewski, B. Yelicich, C. Puppo, X. Y. Liu, M. Czosnyka, C. Y. Wang, V. Novak, R. B. Panerai, and J. A. H. R. Claassen, “Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: The CARNet study,” Medical Engineering & Physics, vol. 36, no. 5, pp. 620-627, May, 2014.

  2. J. A. H. R. Claassen, A. S. S. Meel-van den Abeelen, D. M. Simpson, R. B. Panerai, and I. C. A. R. Network, “Transfer function analysis of dynamic cerebral autoregulation: A white paper from the International Cerebral Autoregulation Research Network,” Journal of Cerebral Blood Flow and Metabolism, vol. 36, no. 4, pp. 665-680, Apr, 2016.

Project 2: Reproducibility of cerebral autoregulation

In this project, we investigated the reproducibility of CA metrics, this time using a variety of different metrics on a common, pooled, data set. The metrics included a large number of methods, both in the time and frequency domains. The different reproducibilities of the various methods were quantified and the effect of other factors, including blood pressure variability, were investigated. The results can be found in [1-3]. As a result of this study, we now know how to assess and to compare the performance of different metrics. Work is under way on publishing a Matlab toolbox that will perform a large number of these analysis methods.

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  1. M. L. Sanders, J. A. H. R. Claassen, M. Aries, E. Bor-Seng-Shu, A. Caicedo, M. Chacon, E. D. Gommer, S. Van Huffel, J. L. Jara, K. Kostoglou, A. Mahdi, V. Z. Marmarelis, G. D. Mitsis, M. Muller, D. A. Nikolic, R. C. Nogueira, S. J. Payne, C. Puppo, D. C. Shin, D. M. Simpson, T. Tarumi, B. Yelicichs, R. Zhang, R. B. Panerai, and J. W. J. Elting, “Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study,” Physiological Measurement, vol. 39, no. 12, Dec, 2018.

  2. M. L. Sanders, J. W. J. Elting, R. B. Panerai, M. Aries, E. Bor-Seng-Shu, A. Caicedo, M. Chacon, E. D. Gommer, S. Van Huffel, J. L. Jara, K. Kostoglou, A. Mahdi, V. Z. Marmarelis, G. D. Mitsis, M. Muller, D. Nikolic, R. C. Nogueira, S. J. Payne, C. Puppo, D. C. Shin, D. M. Simpson, T. Tarumi, B. Yelicich, R. Zhang, and J. Claassen, “Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability,” Front Physiol, vol. 10, pp. 865, 2019.

  3. J. W. Elting, M. L. Sanders, R. B. Panerai, M. Aries, E. Bor-Seng-Shu, A. Caicedo, M. Chacon, E. D. Gommer, S. Van Huffel, J. L. Jara, K. Kostoglou, A. Mahdi, V. Z. Marmarelis, G. D. Mitsis, M. Muller, D. Nikolic, R. C. Nogueira, S. J. Payne, C. Puppo, D. C. Shin, D. M. Simpson, T. Tarumi, B. Yelicich, R. Zhang, and J. Claassen, “Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?,” PLoS One, vol. 15, no. 1, pp. e0227651, 2020.

Introduction

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A major limitation of our field is the lack of a gold standard for assessment of cerebral autoregulation (CA). At our inaugural meeting, it was agreed to set up an international collaborative project to share data and analytical techniques with the objective of improving the reliability of CA metrics and to develop multi-centre agreement on recommendations for estimates of dynamic CA.

The current literature on dynamic CA shows a considerable diversity of analytical techniques, such as transfer function analysis, ARI, Mx and Px indices, neural networks, ARMA modelling, multi-modal pressure-flow, and others. Combined with the different protocols that have been used for assessment of CA (spontaneous fluctuations, thigh cuff manouevres, changes in posture, synchronised breathing, lower-body negative pressure, etc.) and the many different biological factors that can influence CA (posture, blood gases, temperature, sympathetic activity, CMRO2, pathology, etc.) this methodological diversity does not facilitate translation of CA studies to routine clinical use. Furthermore, we currently do not have sufficient evidence from comparisons of methods to support general recommendations for specific experimental protocols and analysis methods.

We have now had two projects successfully come to completion, as described below, with code becoming available for public use. If you are interested in being involved, or would like to propose a new project, do just contact the Chair of CARNet. We will be very happy to discuss potential ideas.

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