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

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...

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Detalles Bibliográficos
Autores principales: Panerai, Ronney B, Brassard, Patrice, Burma, Joel S, Castro, Pedro, Claassen, Jurgen AHR, van Lieshout, Johannes J, Liu, Jia, Lucas, Samuel JE, Minhas, Jatinder S, Mitsis, Georgios D, Nogueira, Ricardo C, Ogoh, Shigehiko, Payne, Stephen J, Rickards, Caroline A, Robertson, Andrew D, Rodrigues, Gabriel D, Smirl, Jonathan D, Simpson, David M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875346/
https://www.ncbi.nlm.nih.gov/pubmed/35962478
http://dx.doi.org/10.1177/0271678X221119760
Descripción
Sumario: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.