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A New Model-Free Index of Dynamic Cerebral Blood Flow Autoregulation

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  • Max Chacón
  • José Luis Jara
  • Ronney B Panerai

Abstract

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free. This new index uses two parameters that are obtained directly from the response signal of the cerebral blood flow velocity to a transient decrease in arterial blood pressure provoked by the sudden release of bilateral thigh cuffs, and a third parameter measuring the difference in slope of this response and the change in arterial blood pressure achieved. With the values of these parameters, a corresponding classic autoregulatory index value could be calculated by using a linear regression model built from theoretical curves generated with the Aaslid-Tiecks model. In 16 healthy subjects who underwent repeated thigh-cuff manoeuvres, the model-free approach exhibited significantly lower intra-subject variability, as measured by the unbiased coefficient of variation, than the classic autoregulatory index (p = 0.032) and the Rate of Return (p

Suggested Citation

  • Max Chacón & José Luis Jara & Ronney B Panerai, 2014. "A New Model-Free Index of Dynamic Cerebral Blood Flow Autoregulation," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0108281
    DOI: 10.1371/journal.pone.0108281
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    References listed on IDEAS

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    1. A. N. Pettitt, 1977. "Testing the Normality of Several Independent Samples Using the Anderson‐Darling Statistic," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(2), pages 156-161, June.
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    1. Max Chacón & José Luis Jara & Rodrigo Miranda & Emmanuel Katsogridakis & Ronney B Panerai, 2018. "Non-linear models for the detection of impaired cerebral blood flow autoregulation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.

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