Evidence map›Paper›PMID 31923919›Full record

ArticlePloS one2020

Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?

Jan Willem Elting, Marit L Sanders, Ronney B Panerai, Marcel Aries, Edson Bor-Seng-Shu, Alexander Caicedo, Max Chacon, Erik D Gommer, Sabine Van Huffel, José L Jara and 15 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.4field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet).Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024
    Review
  8. Article
  9. Depression of dynamic cerebral autoregulation during neural activation: The role of responders and non-responders.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024
    Article
  10. Effects of posture changes on dynamic cerebral autoregulation during early pregnancy in women with obesity and/or sleep apnea.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2023
    Article
  11. 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 · 2023
    Review
  12. Article
  13. Article
  14. Review of studies on dynamic cerebral autoregulation in the acute phase of stroke and the relationship with clinical outcome.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022
    Review
  15. The INfoMATAS project: Methods for assessing cerebral autoregulation in stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022
    Review
  16. Review
  17. Cerebral critical closing pressure and resistance-area product: the influence of dynamic cerebral autoregulation, age and sex.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021
    Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors at 15 institutions in 10 countries.

Jan Willem EltingDepartment of Neurology, University Medical Center Groningen, Groningen, The Netherlands.ORCID 0000-0003-3991-7097
Marit L SandersDepartment of Geriatric Medicine, Radboudumc Alzheimer Centre and Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Ronney B PaneraiDepartment of Cardiovascular Sciences and Leicester Biomedical Research Centre in Cardiovascular Sciences, Glenfield Hospital, Leicester, United Kingdom.
Marcel AriesDepartment of Intensive Care, University of Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands.
Edson Bor-Seng-ShuDepartment of Neurology, Hospital das Clinicas University of Sao Paulo, Sao Paulo, Brazil.
Alexander CaicedoMathematics and Computer Science, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia.
Max ChaconDepartemento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago de Chile, Chile.
Erik D GommerDepartment of Clinical Neurophysiology, University of Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands.ORCID 0000-0003-1054-2682
Sabine Van HuffelDepartment of Electronic Engineering, Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Katholieke Universiteit Leuven, Leuven, Belgium.
José L JaraDepartemento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago de Chile, Chile.
Kyriaki KostoglouDepartment of Electrical, Computer and Software Engineering, McGill University, Montreal, Canada.
Adam MahdiDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.
Vasilis Z MarmarelisDepartment of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
Georgios D MitsisDepartment of Bioengineering, McGill University, Montreal, Canada.
Martin MüllerDepartment of Neurology, Luzerner Kantonsspital, Luzern, Switzerland.
Dragana NikolicInstitute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
Ricardo C NogueiraDepartment of Neurology, Hospital das Clinicas University of Sao Paulo, Sao Paulo, Brazil.
Stephen J PayneDepartment of Engineering Science, University of Oxford, Oxford, United Kingdom.
Corina PuppoDepartamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay.
Dae C ShinDepartment of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
David M SimpsonInstitute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
Takashi TarumiThe Institute for Exercise and Environmental Medicine, Presbyterian Hospital Dallas, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Bernardo YelicichDepartamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay.
Rong ZhangThe Institute for Exercise and Environmental Medicine, Presbyterian Hospital Dallas, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Jurgen A H R ClaassenDepartment of Neurology, University Medical Center Groningen, Groningen, The Netherlands.
Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BRMaastricht University · NLMcGill University · CAPresbyterian Hospital · USUniversidad de la República de Uruguay · UYUniversidad de Santiago de Chile · CLUniversity Medical Center Groningen · NLUniversity of Oxford · GBUniversity of Southampton · GBUniversity of Southern California · USGlenfield Hospital · GBKU Leuven · BELuzerner Kantonsspital · CHRadboud University Nijmegen · NLUniversidad del Rosario · CO

Funding

TrainingP41EB001978 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KHOO, MICHAEL C K · 2003 to 2017
$18.1M
NIBIB NIH HHS P41 EB001978
6 · The paper itself

Abstract

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.

Indexed as

AdultAgedArterial PressureBlood Flow VelocityBlood PressureBlood Pressure DeterminationCerebrovascular CirculationFemaleHealthy VolunteersHomeostasisHumansMaleMiddle AgedMiddle Cerebral ArteryReproducibility of Results

Identifiers

PMID31923919
PMCPMC6954074
OpenAlexW2999404372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.