Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

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dc.creatorPanerai, Ronney
dc.creatorBrassard, Patrice
dc.creatorBurma, Joel S.
dc.creatorCastro, Pedro
dc.creatorClaassen, Jurgen AHR
dc.creatorvan Lieshout, Johannes J.
dc.creatorLiu, Jia
dc.creatorLucas, Samuel JE
dc.creatorMinhas, Jatinder S.
dc.creatorMitsis, Georgios D.
dc.creatorNogueira, Ricardo C.
dc.creatorOgoh, Shigehiko
dc.creatorPayne, Stephen J.
dc.creatorRickards, Caroline A.
dc.creatorRobertson, Andrew D.
dc.creatorRodrigues, Gabriel D.
dc.creatorSmirl, Jonathan D.
dc.creatorSimpson, David M.
dc.creator.orcid0000-0002-5759-6912 (Rickards, Caroline A.)
dc.date.accessioned2023-03-27T15:24:25Z
dc.date.available2023-03-27T15:24:25Z
dc.date.issued2022-08-14
dc.description.abstractCerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.
dc.description.sponsorshipThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Jatinder S. Minhas is funded by an NIHR Clinical Lectureship in Older People and Complex Health Needs. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research, or the Department of Health or the authors' respective organisations.
dc.identifier.citationPanerai, R. B., Brassard, P., Burma, J. S., Castro, P., Claassen, J. A., van Lieshout, J. J., Liu, J., Lucas, S. J., Minhas, J. S., Mitsis, G. D., Nogueira, R. C., Ogoh, S., Payne, S. J., Rickards, C. A., Robertson, A. D., Rodrigues, G. D., Smirl, J. D., Simpson, D. M., & Cerebrovascular Research Network (CARNet) (2023). Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 43(1), 3-25. https://doi.org/10.1177/0271678X221119760
dc.identifier.issn1559-7016
dc.identifier.issue1
dc.identifier.urihttps://hdl.handle.net/20.500.12503/32066
dc.identifier.volume43
dc.publisherInternational Society for Cerebral Blood Flow and Metabolism
dc.relation.urihttps://doi.org/10.1177/0271678X221119760
dc.rights.holder© The Author(s) 2022
dc.rights.licenseAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.sourceJournal of Cerebral Blood Flow and Metabolism
dc.subjectcerebral hemodynamics
dc.subjectconsensus guidelines
dc.subjectreference values
dc.subjecttransfer function analysis
dc.subject.meshReproducibility of Results
dc.subject.meshBrain / blood supply
dc.titleTransfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update
dc.typeArticle
dc.type.materialtext

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