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

Ronney B. Panerai(University of Leicester), Patrice Brassard(Institut universitaire de cardiologie et de pneumologie de Québec), Joel S. Burma(University of Calgary), Pedro Castro(Universidade do Porto), Jurgen A.H.R. Claassen(Radboud University Nijmegen), Johannes J. van Lieshout(University of Nottingham), Jia Liu(Shenzhen University), Samuel J. E. Lucas(University of Birmingham), Jatinder S. Minhas(University of Leicester), Georgios D. Mitsis(McGill University), Ricardo de Carvalho Nogueira(Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo), Shigehiko Ogoh(Toyo University), Stephen J. Payne(National Taiwan University), Caroline A. Rickards(University of North Texas), Andrew D. Robertson(University of Waterloo), Gabriel Dias Rodrigues(University of Milan), Jonathan D. Smirl(University of Calgary), David M. Simpson(University of Southampton)
Journal of Cerebral Blood Flow & Metabolism
August 12, 2022
Cited by 156Open Access
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Abstract

Cerebral 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.


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