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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.

Project 3 During the 6 th annual CARNet meeting (Summer 2016 at MIT, Boston, USA)

The project launch meeting was held for ‘Identifying New Targets for Management and Therapy in Acute Stroke’ (INfoMATAS), which aims to develop guidelines for optimal blood pressure management in acute stroke through a better understanding of the impact of interventions on cerebral perfusion, and the identification and validation of novel therapeutic targets.

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The project yielded a number of review papers in the Special Issue ‘Hemodynamic Physiology in Stroke’ published at the Journal of Cerebral Blood Flow & Metabolism (Volume 42 Issue 3, March 2022 https://journals.sagepub.com/toc/jcba/42/3)

 

1. Claassen. How can integrative physiology advance stroke research and stroke care? [Editorial]. https://doi.org/10.1177/0271678X211057403

2. Nogueira RC, Aries M, Minhas JS, et al. Review of studies on dynamic cerebral autoregulation in the acute phase of stroke and the relationship with clinical outcome. https://doi.org/10.1177/0271678X211045222.
3. Fan, JL, Brassard, P, Rickards, CA, et al. Integrative cerebral blood flow regulation in ischemic stroke. https://doi.org/10.1177/0271678X211032029.
4. Robinson, TG, Minhas, JS, Miller, J. Review of major trials of acute blood pressure management in stroke. https://doi.org/10.1177/0271678X211004310.
5. Llwyd O, Fan J-L and Müller M. Effect of drug interventions on cerebral haemodynamics in ischaemic stroke patients. https://doi.org/10.1177/0271678X211058261.
6. Fan, JL, Nogueira, RC, Brassard, P, et al. Integrative physiological assessment of cerebral hemodynamics and metabolism in acute ischemic stroke. https://doi.org/10.1177/0271678X211033732.
7. Simpson, DM, Payne, SJ, Panerai, RB. The INfoMATAS project: methods for assessing cerebral autoregulation in stroke.
https://doi.org/10.1177/0271678X211029049.

 

As part of this program, participating centres provided Cerebral Autoregulation data collected in acute ischemic stroke patients (arterial pressure and cerebral blood velocity or flow) with the objective of performing a meta-analysis taking into consideration the relationship between Cerebral Autoregulation and clinical outcome. The protocol to undertake this individual patient data meta-analysis (IPD-MA) was published and an article with its results have been
submitted!


7. Beishon L, Minhas JS, Nogueira R, Castro P, Budgeon C, Aries M, Payne S, Robinson TG, Panerai RB. INFOMATAS multi-center systematic review and meta-analysis individual patient data of dynamic cerebral autoregulation in ischemic stroke. Int J Stroke. 2020 Oct;15(7):807-812. https://doi.org/10.1177/1747493020907003.


Currently, an INFOMATAS subgroup are actively engaged in an in-depth examination of data, aiming to unravel correlations between end-tidal carbon dioxide (EtCO 2 ) and patient outcomes, as well as to identify clinically significant cut-off points. If you are interested in this subgroup initiative, please contact Jatinder Minhas on jm591@le.ac.uk. In addition, the group aims to conduct further analysis of the CA data in ischaemic stroke and plans a future multicentre study. If you are interested in participating in this group and/or have any CA data in ischaemic stroke, please contact Ricardo Nogueira on ricardo.nogueira@hc.fm.usp.br.

Project 4

​​There is an active collaboration open to new members that is developing a working group focused on methods in transcranial Doppler ultrasonography (TCD) for measuring neurovascular coupling (NVC). A collaborative literature review is currently underway with the goal of producing recommendations for conducting and advancing research in TCD-measured NVC. These recommendations aim to foster collaborations for developing new projects and methods in TCD-NVC research. If you are interested in joining the working group, please contact Lucy Beishon on lb330@le.ac.uk.

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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