Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability
Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy su...
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Frontiers Media S.A.
2019-07-01
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Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2019.00865/full |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marit L. Sanders Jan Willem J. Elting Ronney B. Panerai Marcel Aries Edson Bor-Seng-Shu Alexander Caicedo Max Chacon Erik D. Gommer Sabine Van Huffel Sabine Van Huffel José L. Jara Kyriaki Kostoglou Adam Mahdi Vasilis Z. Marmarelis Georgios D. Mitsis Martin Müller Dragana Nikolic Ricardo C. Nogueira Stephen J. Payne Corina Puppo Dae C. Shin David M. Simpson Takashi Tarumi Bernardo Yelicich Rong Zhang Jurgen A. H. R. Claassen |
spellingShingle |
Marit L. Sanders Jan Willem J. Elting Ronney B. Panerai Marcel Aries Edson Bor-Seng-Shu Alexander Caicedo Max Chacon Erik D. Gommer Sabine Van Huffel Sabine Van Huffel José L. Jara Kyriaki Kostoglou Adam Mahdi Vasilis Z. Marmarelis Georgios D. Mitsis Martin Müller Dragana Nikolic Ricardo C. Nogueira Stephen J. Payne Corina Puppo Dae C. Shin David M. Simpson Takashi Tarumi Bernardo Yelicich Rong Zhang Jurgen A. H. R. Claassen Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability Frontiers in Physiology ARI index cerebral blood flow cerebral hemodynamics transcranial Doppler transfer function analysis |
author_facet |
Marit L. Sanders Jan Willem J. Elting Ronney B. Panerai Marcel Aries Edson Bor-Seng-Shu Alexander Caicedo Max Chacon Erik D. Gommer Sabine Van Huffel Sabine Van Huffel José L. Jara Kyriaki Kostoglou Adam Mahdi Vasilis Z. Marmarelis Georgios D. Mitsis Martin Müller Dragana Nikolic Ricardo C. Nogueira Stephen J. Payne Corina Puppo Dae C. Shin David M. Simpson Takashi Tarumi Bernardo Yelicich Rong Zhang Jurgen A. H. R. Claassen |
author_sort |
Marit L. Sanders |
title |
Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability |
title_short |
Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability |
title_full |
Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability |
title_fullStr |
Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability |
title_full_unstemmed |
Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability |
title_sort |
dynamic cerebral autoregulation reproducibility is affected by physiological variability |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physiology |
issn |
1664-042X |
publishDate |
2019-07-01 |
description |
Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters. |
topic |
ARI index cerebral blood flow cerebral hemodynamics transcranial Doppler transfer function analysis |
url |
https://www.frontiersin.org/article/10.3389/fphys.2019.00865/full |
work_keys_str_mv |
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doaj-0cd65adba56744b88007071d2a2269092020-11-24T21:49:54ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2019-07-011010.3389/fphys.2019.00865461520Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological VariabilityMarit L. Sanders0Jan Willem J. Elting1Ronney B. Panerai2Marcel Aries3Edson Bor-Seng-Shu4Alexander Caicedo5Max Chacon6Erik D. Gommer7Sabine Van Huffel8Sabine Van Huffel9José L. Jara10Kyriaki Kostoglou11Adam Mahdi12Vasilis Z. Marmarelis13Georgios D. Mitsis14Martin Müller15Dragana Nikolic16Ricardo C. Nogueira17Stephen J. Payne18Corina Puppo19Dae C. Shin20David M. Simpson21Takashi Tarumi22Bernardo Yelicich23Rong Zhang24Jurgen A. H. R. Claassen25Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, NetherlandsDepartment of Neurology, University Medical Center Groningen, Groningen, NetherlandsDepartment of Cardiovascular Sciences, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United KingdomDepartment of Intensive Care, University of Maastricht, Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Neurology, Faculty of Medicine, Hospital das Clinicas University of São Paulo, São Paulo, BrazilDepartment of Applied Mathematics and Computer Science, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogotá, ColombiaDepartment of Engineering Informatics, Institute of Biomedical Engineering, University of Santiago, Santiago, ChileDepartment of Clinical Neurophysiology, Maastricht University Medical Centre, Maastricht, NetherlandsDepartment of Electronic Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Katholieke Universiteit Leuven, Leuven, Belgium0Interuniversity Microelectronics Centre, Leuven, BelgiumDepartment of Engineering Informatics, Institute of Biomedical Engineering, University of Santiago, Santiago, Chile1Department of Electrical, Computer and Software Engineering, McGill University, Montreal, QC, Canada2Department of Engineering Science, University of Oxford, Oxford, United Kingdom3Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States4Department of Bioengineering, McGill University, Montreal, QC, Canada5Department of Neurology, Luzerner Kantonsspital, Luzern, Switzerland6Faculty of Engineering and the Environment, Institute of Sound and Vibration Research, University of Southampton, Southampton, United KingdomDepartment of Neurology, Faculty of Medicine, Hospital das Clinicas University of São Paulo, São Paulo, Brazil2Department of Engineering Science, University of Oxford, Oxford, United Kingdom7Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay3Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States6Faculty of Engineering and the Environment, Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom8Institute for Exercise and Environmental Medicine, Presbyterian Hospital of Dallas, University of Texas Southwestern Medical Center, Dallas, TX, United States7Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay8Institute for Exercise and Environmental Medicine, Presbyterian Hospital of Dallas, University of Texas Southwestern Medical Center, Dallas, TX, United StatesDepartment of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, NetherlandsParameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.https://www.frontiersin.org/article/10.3389/fphys.2019.00865/fullARI indexcerebral blood flowcerebral hemodynamicstranscranial Dopplertransfer function analysis |