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, Kyriaki Kostoglou, Adam Mahdi, Vasilis Z. Marmarelis, Georgios D. Mitsis, Martin Müller, Dragana Nikolic, Ricardo C. Nogueira, Stephen J. Payne, Corina Puppo, Dae C. ShinDavid M. Simpson, Takashi Tarumi, Bernardo Yelicich, Rong Zhang, Jurgen A.H.R. Claassen

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Article numbere0227651
Pages (from-to)e0227651
Issue number1
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • General


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